{"id":"W6889644626","doi":"10.25824/redu/efpq5w","title":"Data referring to the characterization of healthy subjects from the Calgary Normative Study (CNS) using texture-based brain networks from structural MRI","year":2025,"lang":"en","type":"dataset","venue":"University of Campinas","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Normative; Set (abstract data type); Magnetic resonance imaging; Functional magnetic resonance imaging; Neuroimaging","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0008873863,0.002293431,0.001617227,0.002748346,0.0008268459,0.001502805,0.00271144,0.00220357,0.02894159],"category_scores_gemma":[0.005890583,0.0005240796,0.001065495,0.003744655,0.0004467415,0.0005919075,0.001542038,0.001373814,0.03580537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001206651,"about_ca_system_score_gemma":0.001914762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04682962,"about_ca_topic_score_gemma":0.085899,"domain_scores_codex":[0.9991865,0.000127081,0.0001009255,0.0002642551,0.0001910308,0.000130206],"domain_scores_gemma":[0.9982781,0.0004801477,0.0001748564,0.0004008037,0.0004841878,0.0001818992],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0002212996,0.00006139479,0.002807382,0.0007174974,0.00008167565,0.00008537271,0.00003453731,0.0003772573,0.0002499819,0.0002870107,0.9894074,0.005669217],"study_design_scores_gemma":[0.0008997162,0.00009538966,0.04614076,0.0009927429,0.0002166028,0.000602864,0.0002420849,0.001756329,0.001181042,0.002579454,0.9451981,0.00009496551],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0007911423,0.0001549319,0.0001368365,0.00005658303,0.00003658721,0.00002369827,0.9980555,0.0002007715,0.0005440793],"genre_scores_gemma":[0.0009716923,0.00006623942,0.0002112777,0.00002728822,0.000009205265,0.00008828845,0.9981199,0.00002770944,0.0004783136],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04682962,"threshold_uncertainty_score":0.09681928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03367089631308532,"score_gpt":0.2786889602707188,"score_spread":0.2450180639576335,"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."}}