[
  {
    "canonical_title": "Deep Residual Learning for Image Recognition",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.25,
        "metric": "citation_count",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W2194775991",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 222154.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W2194775991",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 1,
    "scenario": "governance_practitioner",
    "score": 0.025,
    "work_id": "paper-0079",
    "work_type": "paper"
  },
  {
    "canonical_title": "Random Forests",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.142126,
        "metric": "citation_count",
        "normalized": 0.568502,
        "provenance_url": "https://openalex.org/W2911964244",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 126295.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W2911964244",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 2,
    "scenario": "governance_practitioner",
    "score": -0.082874,
    "work_id": "paper-0043",
    "work_type": "paper"
  },
  {
    "canonical_title": "Long Short-Term Memory",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.110459,
        "metric": "citation_count",
        "normalized": 0.441838,
        "provenance_url": "https://openalex.org/W2064675550",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 98156.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W2064675550",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 3,
    "scenario": "governance_practitioner",
    "score": -0.114541,
    "work_id": "paper-0038",
    "work_type": "paper"
  },
  {
    "canonical_title": "Adam: A Method for Stochastic Optimization",
    "components": [
      {
        "confidence": "medium",
        "contribution": 0.095411,
        "metric": "citation_count",
        "normalized": 0.381645,
        "provenance_url": "https://openalex.org/W1522301498",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 84784.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "low",
        "contribution": 0.092857,
        "metric": "readership_persistence",
        "normalized": 0.928571,
        "provenance_url": "https://openalex.org/W1522301498",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 14.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 4,
    "scenario": "governance_practitioner",
    "score": -0.136732,
    "work_id": "paper-0070",
    "work_type": "paper"
  },
  {
    "canonical_title": "Fuzzy Sets",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.074444,
        "metric": "citation_count",
        "normalized": 0.297775,
        "provenance_url": "https://openalex.org/W4211007335",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 66152.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W4211007335",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 5,
    "scenario": "governance_practitioner",
    "score": -0.150556,
    "work_id": "paper-0012",
    "work_type": "paper"
  },
  {
    "canonical_title": "Gradient-Based Learning Applied to Document Recognition",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.065551,
        "metric": "citation_count",
        "normalized": 0.262205,
        "provenance_url": "https://openalex.org/W2112796928",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 58250.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W2112796928",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 6,
    "scenario": "governance_practitioner",
    "score": -0.159449,
    "work_id": "paper-0041",
    "work_type": "paper"
  },
  {
    "canonical_title": "Regression Shrinkage and Selection via the Lasso",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.058306,
        "metric": "citation_count",
        "normalized": 0.233226,
        "provenance_url": "https://openalex.org/W2135046866",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 51812.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W2135046866",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 7,
    "scenario": "governance_practitioner",
    "score": -0.166694,
    "work_id": "paper-0037",
    "work_type": "paper"
  },
  {
    "canonical_title": "Going Deeper with Convolutions",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.052706,
        "metric": "citation_count",
        "normalized": 0.210822,
        "provenance_url": "https://openalex.org/W2097117768",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 46835.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.092857,
        "metric": "readership_persistence",
        "normalized": 0.928571,
        "provenance_url": "https://openalex.org/W2097117768",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 14.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 8,
    "scenario": "governance_practitioner",
    "score": -0.179437,
    "work_id": "paper-0068",
    "work_type": "paper"
  },
  {
    "canonical_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.038576,
        "metric": "citation_count",
        "normalized": 0.154303,
        "provenance_url": "https://openalex.org/W2095705004",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 34279.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W2095705004",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 9,
    "scenario": "governance_practitioner",
    "score": -0.186424,
    "work_id": "paper-0061",
    "work_type": "paper"
  },
  {
    "canonical_title": "GloVe: Global Vectors for Word Representation",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.038007,
        "metric": "citation_count",
        "normalized": 0.15203,
        "provenance_url": "https://openalex.org/W2250539671",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 33774.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W2250539671",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 10,
    "scenario": "governance_practitioner",
    "score": -0.186993,
    "work_id": "paper-0065",
    "work_type": "paper"
  },
  {
    "canonical_title": "Support-Vector Networks",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.037199,
        "metric": "citation_count",
        "normalized": 0.148798,
        "provenance_url": "https://openalex.org/W2119821739",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 33056.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W2119821739",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 11,
    "scenario": "governance_practitioner",
    "score": -0.187801,
    "work_id": "paper-0035",
    "work_type": "paper"
  },
  {
    "canonical_title": "Learning Representations by Back-Propagating Errors",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.034782,
        "metric": "citation_count",
        "normalized": 0.139129,
        "provenance_url": "https://openalex.org/W1498436455",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 30908.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W1498436455",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 12,
    "scenario": "governance_practitioner",
    "score": -0.190218,
    "work_id": "paper-0024",
    "work_type": "paper"
  },
  {
    "canonical_title": "Greedy Function Approximation: A Gradient Boosting Machine",
    "components": [
      {
        "confidence": "medium",
        "contribution": 0.032635,
        "metric": "citation_count",
        "normalized": 0.13054,
        "provenance_url": "https://openalex.org/W1678356000",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 29000.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "low",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W1678356000",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 13,
    "scenario": "governance_practitioner",
    "score": -0.192365,
    "work_id": "paper-0045",
    "work_type": "paper"
  },
  {
    "canonical_title": "Latent Dirichlet Allocation",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.030442,
        "metric": "citation_count",
        "normalized": 0.121767,
        "provenance_url": "https://openalex.org/W1880262756",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 27051.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W1880262756",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 14,
    "scenario": "governance_practitioner",
    "score": -0.194558,
    "work_id": "paper-0047",
    "work_type": "paper"
  },
  {
    "canonical_title": "Human-Level Control Through Deep Reinforcement Learning",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.033756,
        "metric": "citation_count",
        "normalized": 0.135023,
        "provenance_url": "https://openalex.org/W2145339207",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 29996.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.092857,
        "metric": "readership_persistence",
        "normalized": 0.928571,
        "provenance_url": "https://openalex.org/W2145339207",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 14.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 15,
    "scenario": "governance_practitioner",
    "score": -0.198387,
    "work_id": "paper-0076",
    "work_type": "paper"
  },
  {
    "canonical_title": "Reducing the Dimensionality of Data with Neural Networks",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.023594,
        "metric": "citation_count",
        "normalized": 0.094376,
        "provenance_url": "https://openalex.org/W2100495367",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 20966.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W2100495367",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 16,
    "scenario": "governance_practitioner",
    "score": -0.201406,
    "work_id": "paper-0050",
    "work_type": "paper"
  },
  {
    "canonical_title": "A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.022875,
        "metric": "citation_count",
        "normalized": 0.0915,
        "provenance_url": "https://openalex.org/W1988790447",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 20327.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W1988790447",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 17,
    "scenario": "governance_practitioner",
    "score": -0.202125,
    "work_id": "paper-0039",
    "work_type": "paper"
  },
  {
    "canonical_title": "A Logical Calculus of the Ideas Immanent in Nervous Activity",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.020254,
        "metric": "citation_count",
        "normalized": 0.081016,
        "provenance_url": "https://openalex.org/W1995341919",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 17998.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W1995341919",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 18,
    "scenario": "governance_practitioner",
    "score": -0.204746,
    "work_id": "paper-0001",
    "work_type": "paper"
  },
  {
    "canonical_title": "A Fast Learning Algorithm for Deep Belief Nets",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.018464,
        "metric": "citation_count",
        "normalized": 0.073854,
        "provenance_url": "https://openalex.org/W2136922672",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 16407.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W2136922672",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 19,
    "scenario": "governance_practitioner",
    "score": -0.206536,
    "work_id": "paper-0049",
    "work_type": "paper"
  },
  {
    "canonical_title": "Auto-Encoding Variational Bayes",
    "components": [
      {
        "confidence": "medium",
        "contribution": 0.017587,
        "metric": "citation_count",
        "normalized": 0.070348,
        "provenance_url": "https://openalex.org/W1959608418",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15628.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "low",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W1959608418",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 20,
    "scenario": "governance_practitioner",
    "score": -0.207413,
    "work_id": "paper-0063",
    "work_type": "paper"
  },
  {
    "canonical_title": "No Free Lunch Theorems for Optimization",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.015687,
        "metric": "citation_count",
        "normalized": 0.062749,
        "provenance_url": "https://openalex.org/W2151554678",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 13940.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W2151554678",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 21,
    "scenario": "governance_practitioner",
    "score": -0.209313,
    "work_id": "paper-0040",
    "work_type": "paper"
  },
  {
    "canonical_title": "The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain",
    "components": [
      {
        "confidence": "medium",
        "contribution": 0.013237,
        "metric": "citation_count",
        "normalized": 0.05295,
        "provenance_url": "https://openalex.org/W2040870580",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 11763.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "low",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W2040870580",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 22,
    "scenario": "governance_practitioner",
    "score": -0.211763,
    "work_id": "paper-0006",
    "work_type": "paper"
  },
  {
    "canonical_title": "Matrix Factorization Techniques for Recommender Systems",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.013147,
        "metric": "citation_count",
        "normalized": 0.05259,
        "provenance_url": "https://openalex.org/W2054141820",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 11683.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W2054141820",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 23,
    "scenario": "governance_practitioner",
    "score": -0.211853,
    "work_id": "paper-0053",
    "work_type": "paper"
  },
  {
    "canonical_title": "Self-Organized Formation of Topologically Correct Feature Maps",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.010713,
        "metric": "citation_count",
        "normalized": 0.042853,
        "provenance_url": "https://openalex.org/W65738273",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 9520.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W65738273",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 24,
    "scenario": "governance_practitioner",
    "score": -0.214287,
    "work_id": "paper-0021",
    "work_type": "paper"
  },
  {
    "canonical_title": "Mastering the Game of Go with Deep Neural Networks and Tree Search",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.01782,
        "metric": "citation_count",
        "normalized": 0.071279,
        "provenance_url": "https://openalex.org/W2257979135",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15835.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.092857,
        "metric": "readership_persistence",
        "normalized": 0.928571,
        "provenance_url": "https://openalex.org/W2257979135",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 14.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 25,
    "scenario": "governance_practitioner",
    "score": -0.214323,
    "work_id": "paper-0082",
    "work_type": "paper"
  },
  {
    "canonical_title": "Multilayer Feedforward Networks Are Universal Approximators",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.010535,
        "metric": "citation_count",
        "normalized": 0.042142,
        "provenance_url": "https://openalex.org/W3146803896",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 9362.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W3146803896",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 26,
    "scenario": "governance_practitioner",
    "score": -0.214465,
    "work_id": "paper-0027",
    "work_type": "paper"
  },
  {
    "canonical_title": "Q-Learning",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.010098,
        "metric": "citation_count",
        "normalized": 0.040391,
        "provenance_url": "https://openalex.org/W32403112",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 8973.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W32403112",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 27,
    "scenario": "governance_practitioner",
    "score": -0.214902,
    "work_id": "paper-0032",
    "work_type": "paper"
  },
  {
    "canonical_title": "Neural Machine Translation by Jointly Learning to Align and Translate",
    "components": [
      {
        "confidence": "medium",
        "contribution": 0.016453,
        "metric": "citation_count",
        "normalized": 0.06581,
        "provenance_url": "https://openalex.org/W2133564696",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 14620.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "low",
        "contribution": 0.092857,
        "metric": "readership_persistence",
        "normalized": 0.928571,
        "provenance_url": "https://openalex.org/W2133564696",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 14.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 28,
    "scenario": "governance_practitioner",
    "score": -0.21569,
    "work_id": "paper-0075",
    "work_type": "paper"
  },
  {
    "canonical_title": "A Robust Layered Control System for a Mobile Robot",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.008723,
        "metric": "citation_count",
        "normalized": 0.03489,
        "provenance_url": "https://openalex.org/W2097856935",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 7751.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W2097856935",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 29,
    "scenario": "governance_practitioner",
    "score": -0.216277,
    "work_id": "paper-0023",
    "work_type": "paper"
  },
  {
    "canonical_title": "Minds, Brains, and Programs",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.007299,
        "metric": "citation_count",
        "normalized": 0.029196,
        "provenance_url": "https://openalex.org/W2251410821",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 6486.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W2251410821",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 30,
    "scenario": "governance_practitioner",
    "score": -0.217701,
    "work_id": "paper-0018",
    "work_type": "paper"
  },
  {
    "canonical_title": "The PageRank Citation Ranking: Bringing Order to the Web",
    "components": [
      {
        "confidence": "medium",
        "contribution": 0.014269,
        "metric": "citation_count",
        "normalized": 0.057078,
        "provenance_url": "https://openalex.org/W1854214752",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 12680.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "low",
        "contribution": 0.092857,
        "metric": "readership_persistence",
        "normalized": 0.928571,
        "provenance_url": "https://openalex.org/W1854214752",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 14.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 31,
    "scenario": "governance_practitioner",
    "score": -0.217874,
    "work_id": "paper-0042",
    "work_type": "paper"
  },
  {
    "canonical_title": "Intelligence Without Representation",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.0053,
        "metric": "citation_count",
        "normalized": 0.021202,
        "provenance_url": "https://openalex.org/W2397253692",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 4710.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W2397253692",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 32,
    "scenario": "governance_practitioner",
    "score": -0.2197,
    "work_id": "paper-0031",
    "work_type": "paper"
  },
  {
    "canonical_title": "Some Studies in Machine Learning Using the Game of Checkers",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.00491,
        "metric": "citation_count",
        "normalized": 0.01964,
        "provenance_url": "https://openalex.org/W3198350258",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 4363.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W3198350258",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 33,
    "scenario": "governance_practitioner",
    "score": -0.22009,
    "work_id": "paper-0007",
    "work_type": "paper"
  },
  {
    "canonical_title": "A Mathematical Theory of Communication",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.010963,
        "metric": "citation_count",
        "normalized": 0.043852,
        "provenance_url": "https://openalex.org/W2041404167",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 9742.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.092857,
        "metric": "readership_persistence",
        "normalized": 0.928571,
        "provenance_url": "https://openalex.org/W2041404167",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 14.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 34,
    "scenario": "governance_practitioner",
    "score": -0.22118,
    "work_id": "paper-0003",
    "work_type": "paper"
  },
  {
    "canonical_title": "Fairness Through Awareness",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.003803,
        "metric": "citation_count",
        "normalized": 0.01521,
        "provenance_url": "https://openalex.org/W2100960835",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 3379.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W2100960835",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 35,
    "scenario": "governance_practitioner",
    "score": -0.221197,
    "work_id": "paper-0058",
    "work_type": "paper"
  },
  {
    "canonical_title": "Some Philosophical Problems from the Standpoint of Artificial Intelligence",
    "components": [
      {
        "confidence": "medium",
        "contribution": 0.003719,
        "metric": "citation_count",
        "normalized": 0.014877,
        "provenance_url": "https://openalex.org/W2138162238",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 3305.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "low",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W2138162238",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 36,
    "scenario": "governance_practitioner",
    "score": -0.221281,
    "work_id": "paper-0014",
    "work_type": "paper"
  },
  {
    "canonical_title": "A Few Useful Things to Know About Machine Learning",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.003657,
        "metric": "citation_count",
        "normalized": 0.014629,
        "provenance_url": "https://openalex.org/W2161336914",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 3250.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W2161336914",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 37,
    "scenario": "governance_practitioner",
    "score": -0.221343,
    "work_id": "paper-0059",
    "work_type": "paper"
  },
  {
    "canonical_title": "ImageNet: A Large-Scale Hierarchical Image Database",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.003314,
        "metric": "citation_count",
        "normalized": 0.013257,
        "provenance_url": "https://openalex.org/W4239072543",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 2945.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W4239072543",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 38,
    "scenario": "governance_practitioner",
    "score": -0.221686,
    "work_id": "paper-0054",
    "work_type": "paper"
  },
  {
    "canonical_title": "Finding Structure in Time",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.003296,
        "metric": "citation_count",
        "normalized": 0.013185,
        "provenance_url": "https://openalex.org/W4254816979",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 2929.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W4254816979",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 39,
    "scenario": "governance_practitioner",
    "score": -0.221704,
    "work_id": "paper-0030",
    "work_type": "paper"
  },
  {
    "canonical_title": "Learning to Predict by the Methods of Temporal Differences",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.003146,
        "metric": "citation_count",
        "normalized": 0.012586,
        "provenance_url": "https://openalex.org/W3041202696",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 2796.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W3041202696",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 40,
    "scenario": "governance_practitioner",
    "score": -0.221854,
    "work_id": "paper-0026",
    "work_type": "paper"
  },
  {
    "canonical_title": "A Neural Probabilistic Language Model",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.00301,
        "metric": "citation_count",
        "normalized": 0.012041,
        "provenance_url": "https://openalex.org/W2998704965",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 2675.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W2998704965",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 41,
    "scenario": "governance_practitioner",
    "score": -0.22199,
    "work_id": "paper-0048",
    "work_type": "paper"
  },
  {
    "canonical_title": "The Symbol Grounding Problem",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.00288,
        "metric": "citation_count",
        "normalized": 0.011519,
        "provenance_url": "https://openalex.org/W2107019937",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 2559.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W2107019937",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 42,
    "scenario": "governance_practitioner",
    "score": -0.22212,
    "work_id": "paper-0029",
    "work_type": "paper"
  },
  {
    "canonical_title": "Fusion, Propagation, and Structuring in Belief Networks",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.002422,
        "metric": "citation_count",
        "normalized": 0.009687,
        "provenance_url": "https://openalex.org/W2143075689",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 2152.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W2143075689",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 43,
    "scenario": "governance_practitioner",
    "score": -0.222578,
    "work_id": "paper-0025",
    "work_type": "paper"
  },
  {
    "canonical_title": "Deep Neural Networks for Acoustic Modeling in Speech Recognition",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.002143,
        "metric": "citation_count",
        "normalized": 0.008571,
        "provenance_url": "https://openalex.org/W2184045248",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 1904.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W2184045248",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 44,
    "scenario": "governance_practitioner",
    "score": -0.222857,
    "work_id": "paper-0057",
    "work_type": "paper"
  },
  {
    "canonical_title": "Man-Computer Symbiosis",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.00202,
        "metric": "citation_count",
        "normalized": 0.00808,
        "provenance_url": "https://openalex.org/W2141373701",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 1795.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W2141373701",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 45,
    "scenario": "governance_practitioner",
    "score": -0.22298,
    "work_id": "paper-0009",
    "work_type": "paper"
  },
  {
    "canonical_title": "The Unreasonable Effectiveness of Data",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.002017,
        "metric": "citation_count",
        "normalized": 0.008066,
        "provenance_url": "https://openalex.org/W2103018059",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 1792.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W2103018059",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 46,
    "scenario": "governance_practitioner",
    "score": -0.222983,
    "work_id": "paper-0055",
    "work_type": "paper"
  },
  {
    "canonical_title": "Computing Machinery and Intelligence",
    "components": [
      {
        "confidence": "medium",
        "contribution": 0.00152,
        "metric": "citation_count",
        "normalized": 0.006081,
        "provenance_url": "https://openalex.org/W4307979480",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 1351.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "low",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W4307979480",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 47,
    "scenario": "governance_practitioner",
    "score": -0.22348,
    "work_id": "paper-0004",
    "work_type": "paper"
  },
  {
    "canonical_title": "Statistical Modeling: The Two Cultures",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.001514,
        "metric": "citation_count",
        "normalized": 0.006054,
        "provenance_url": "https://openalex.org/W2160455305",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 1345.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W2160455305",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 48,
    "scenario": "governance_practitioner",
    "score": -0.223486,
    "work_id": "paper-0044",
    "work_type": "paper"
  },
  {
    "canonical_title": "Distilling the Knowledge in a Neural Network",
    "components": [
      {
        "confidence": "high",
        "contribution": 0.015718,
        "metric": "citation_count",
        "normalized": 0.062871,
        "provenance_url": "https://openalex.org/W1821462560",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 13967.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "medium",
        "contribution": 0.085714,
        "metric": "readership_persistence",
        "normalized": 0.857143,
        "provenance_url": "https://openalex.org/W1821462560",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 13.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 49,
    "scenario": "governance_practitioner",
    "score": -0.223568,
    "work_id": "paper-0073",
    "work_type": "paper"
  },
  {
    "canonical_title": "GPS: A Program That Simulates Human Thought",
    "components": [
      {
        "confidence": "medium",
        "contribution": 0.000924,
        "metric": "citation_count",
        "normalized": 0.003696,
        "provenance_url": "https://openalex.org/W1923807516",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 821.0,
        "weight": 0.25
      },
      {
        "metric": "library_holdings",
        "missing_data_penalty": 0.15,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.3
      },
      {
        "confidence": "low",
        "contribution": 0.1,
        "metric": "readership_persistence",
        "normalized": 1.0,
        "provenance_url": "https://openalex.org/W1923807516",
        "retrieved_at": "2026-06-29",
        "source": "OpenAlex",
        "status": "present",
        "value": 15.0,
        "weight": 0.1
      },
      {
        "metric": "syllabus_adoptions",
        "missing_data_penalty": 0.175,
        "note": "recorded as missing; penalized by rule, never imputed",
        "status": "missing",
        "weight": 0.35
      }
    ],
    "conflict_flag": false,
    "rank": 50,
    "scenario": "governance_practitioner",
    "score": -0.224076,
    "work_id": "paper-0011",
    "work_type": "paper"
  }
]
