- 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"
}- 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
- evidence_refs
[
{
"ref": "paper:paper-7a933b7da09d"
},
{
"ref": "paper:paper-d7dd6ae02de1"
}
]- 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"
}