{"id":"W2114549884","doi":"10.1093/cercor/bhn003","title":"Revealing Modular Architecture of Human Brain Structural Networks by Using Cortical Thickness from MRI","year":2008,"lang":"en","type":"article","venue":"Cerebral Cortex","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":491,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"National Institute of Mental Health; Canadian Institutes of Health Research; Killam Trusts","keywords":"Modularity (biology); Modular design; Human brain; Neuroscience; Computer science; Architecture; Cortex (anatomy); Psychology; Biology; Evolutionary biology; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000420505,0.0004013486,0.0001715374,0.002565311,0.0001686597,0.000473862,0.0002079955,0.0002381025,0.000615388],"category_scores_gemma":[0.002490342,0.0002578028,0.000259598,0.001003516,0.0004044906,0.0007233318,0.0004214889,0.0002542235,0.0001044154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001984606,"about_ca_system_score_gemma":0.0001908721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001743637,"about_ca_topic_score_gemma":0.002918645,"domain_scores_codex":[0.9998722,0.00004387406,0.0000071521,0.00003376997,0.00002402706,0.00001887541],"domain_scores_gemma":[0.9993305,0.0002382284,0.0002272285,0.00008146354,0.00007668843,0.00004596689],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0007833539,0.00007049822,0.222181,0.0005430741,0.000938058,0.001415363,0.001640934,0.05190839,0.506119,0.01083379,0.001240135,0.2023263],"study_design_scores_gemma":[0.00003388285,0.0001514232,0.7271397,0.00006659702,0.0002847217,0.002821964,0.0004372488,0.1967586,0.04504133,0.02568695,0.00149045,0.0000872803],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9355582,0.0004046005,0.06215357,0.00009384398,0.00000650129,0.00002613522,0.0003801816,0.000137851,0.001239073],"genre_scores_gemma":[0.9816725,0.0002444411,0.01766546,0.00001267485,0.00001303987,0.00001506552,0.000223647,0.0000200245,0.0001332763],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002565311,"threshold_uncertainty_score":0.003466904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03672264118669128,"score_gpt":0.2733953957046122,"score_spread":0.2366727545179209,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}