Version history

{
  "scope": "mouse and macaque cortex, retrograde tracer interareal connectivity data",
  "claim_text": "Across mouse and macaque, weighted interareal cortical connectivity is highly predictable: medium- and strong-weight links can be predicted from network features with ~85-90% accuracy in mouse (70-80% in macaque), whereas weak links remain unpredictable.",
  "raw_fields": {
    "n": null,
    "doi": "10.1162/netn_a_00345",
    "claim": "Across mouse and macaque, weighted interareal cortical connectivity is highly predictable: medium- and strong-weight links can be predicted from network features with ~85-90% accuracy in mouse (70-80% in macaque), whereas weak links remain unpredictable.",
    "cite_key": "Molnar2024",
    "evidence": "Machine-learning predictability framework applied to retrograde tract-tracing-derived weighted interareal cortical matrices in mouse and macaque.",
    "effect_size": "binary link AUC ≥ 0.8 (macaque); medium/strong weighted link accuracy 85-90% (mouse), 70-80% (macaque)",
    "text_access": "abstract_only",
    "study_system": "mouse and macaque cortex, retrograde tracer interareal connectivity data",
    "argument_role": "supporting",
    "replication_status": "replication_unknown",
    "claim_source_sentence": "Weighted medium and strong links are predictable with an 85%-90% accuracy (mouse) and 70%-80% (macaque), whereas weak links are not predictable in either species.",
    "source_provenance_status": "non_substring_match",
    "replication_evidence_dois": [],
    "effect_size_source_sentence": "At the binary level, links are predictable with an area under the ROC curve of at least 0.8 for the macaque. Weighted medium and strong links are predictable with an 85%-90% accuracy (mouse) and 70%-80% (macaque), whereas weak links are not predictable in either species."
  },
  "section_id": "section_02",
  "source_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_02_evidence_package.json",
  "effect_size": "binary link AUC ≥ 0.8 (macaque); medium/strong weighted link accuracy 85-90% (mouse), 70-80% (macaque)",
  "review_repo": "ComputationalReviewRecurrence",
  "section_ref": "wiki_page:computationalreviewrecurrence-02-anatomy-primer",
  "source_kind": "review_finding",
  "source_path": "evidence/section_02_evidence_package.json",
  "source_refs": [
    "paper:paper-d7dd6ae02de1"
  ],
  "source_span": "Weighted medium and strong links are predictable with an 85%-90% accuracy (mouse) and 70%-80% (macaque), whereas weak links are not predictable in either species.",
  "study_system": "mouse and macaque cortex, retrograde tracer interareal connectivity data",
  "evidence_refs": [
    {
      "ref": "paper:paper-d7dd6ae02de1"
    }
  ],
  "section_title": "2. Mouse-cortex anatomy primer — areal map, layer structure, projection-class nomenclature (IT / PT / CT), tools available for E→E dissection (paired patch, Cre lines, two-photon-targeted patch, optogenetics, EM reconstruction)",
  "source_policy": {
    "mode": "public_source_pointer_with_short_context",
    "notes": [
      "Local review repositories are read-only inputs.",
      "SciDEX stores paper metadata, structured evidence, file pointers, and short citation contexts; it does not copy full review prose."
    ],
    "source_commit_sha": "79ce062d54a924ce05953ec90aa9d26044d2b48f",
    "source_repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence"
  },
  "evidence_summary": "Machine-learning predictability framework applied to retrograde tract-tracing-derived weighted interareal cortical matrices in mouse and macaque.",
  "review_bundle_ref": "analysis_bundle:ab-d9c479db9be9",
  "replication_status": "replication_unknown",
  "review_package_ref": "analysis_bundle:ab-d9c479db9be9",
  "source_artifact_ref": "wiki_page:computationalreviewrecurrence-02-anatomy-primer",
  "origin_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_02_evidence_package.json",
  "commit_sha": "79ce062d54a924ce05953ec90aa9d26044d2b48f",
  "created_by": "persona-jerome-lecoq-gbo-neuroscience",
  "repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence"
}