Version history

{
  "scope": "Cross-species mammalian cortico-cortical networks (mouse, macaque)",
  "claim_text": "Mammalian cortico-cortical networks have unusually high binary connection density — 0.66 in macaque and 0.97 in mouse — consistent with an exponential distance rule for connection probability.",
  "raw_fields": {
    "n": 0,
    "doi": "10.1162/netn_a_00345",
    "claim": "Mammalian cortico-cortical networks have unusually high binary connection density — 0.66 in macaque and 0.97 in mouse — consistent with an exponential distance rule for connection probability.",
    "cite_key": "Molnar2024",
    "evidence": "Documents the universal high cortical connection density and exponential distance rule across species.",
    "effect_size": "Binary connection density: 0.66 macaque, 0.97 mouse",
    "text_access": "fulltext",
    "study_system": "Cross-species mammalian cortico-cortical networks (mouse, macaque)",
    "source_cluster_id": "cluster_11",
    "replication_status": "independently_replicated",
    "claim_source_sentence": "One key distinguishing feature is their high density of binary connectivity (connections existing or not), that is, they contain a large fraction of the maximum number of possible connections: 0.66 for the macaque ( Markov et al., 2011 ) and 0.97 for the mouse ( Gămănuţ et al., 2",
    "replication_evidence_dois": [
      "10.1093/cercor/bhs270",
      "10.1162/netn_a_00159"
    ],
    "effect_size_source_sentence": "One key distinguishing feature is their high density of binary connectivity (connections existing or not), that is, they contain a large fraction of the maximum number of possible connections: 0.66 for the macaque ( Markov et al., 2011 ) and 0.97 for the mouse ( Gămănuţ et al., 2"
  },
  "section_id": "section_12",
  "source_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewLoops/blob/0632aae8abc141909207fe91f6349b9e36489c3b/evidence/section_12_evidence_package.json",
  "effect_size": "Binary connection density: 0.66 macaque, 0.97 mouse",
  "review_repo": "ComputationalReviewLoops",
  "section_ref": "wiki_page:computationalreviewloops-12",
  "source_kind": "review_finding",
  "source_path": "evidence/section_12_evidence_package.json",
  "source_refs": [
    "paper:paper-7a933b7da09d",
    "paper:paper-d7dd6ae02de1"
  ],
  "source_span": "One key distinguishing feature is their high density of binary connectivity (connections existing or not), that is, they contain a large fraction of the maximum number of possible connections: 0.66 for the macaque ( Markov et al., 2011 ) and 0.97 for the mouse ( Gămănuţ et al., 2",
  "study_system": "Cross-species mammalian cortico-cortical networks (mouse, macaque)",
  "evidence_refs": [
    {
      "ref": "paper:paper-7a933b7da09d"
    },
    {
      "ref": "paper:paper-d7dd6ae02de1"
    }
  ],
  "section_title": "Whole-Brain Connectomics: Mouse, Primate, Human",
  "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": "0632aae8abc141909207fe91f6349b9e36489c3b",
    "source_repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewLoops"
  },
  "evidence_summary": "Documents the universal high cortical connection density and exponential distance rule across species.",
  "review_bundle_ref": "analysis_bundle:ab-d49e54403ef9",
  "replication_status": "independently_replicated",
  "review_package_ref": "analysis_bundle:ab-d49e54403ef9",
  "source_artifact_ref": "wiki_page:computationalreviewloops-12",
  "origin_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewLoops/blob/0632aae8abc141909207fe91f6349b9e36489c3b/evidence/section_12_evidence_package.json",
  "commit_sha": "0632aae8abc141909207fe91f6349b9e36489c3b",
  "created_by": "persona-jerome-lecoq-gbo-neuroscience",
  "repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewLoops"
}