{"id":"W6889749519","doi":"10.25824/redu/bquibt","title":"Texture networks generated for 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; Texture (cosmology); Set (abstract data type); Functional magnetic resonance imaging; Characterization (materials science); Pattern recognition (psychology); Magnetic resonance imaging","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":[0.0006569833,0.002316791,0.0009781977,0.002821322,0.0006115615,0.001155778,0.002118419,0.001849198,0.01505035],"category_scores_gemma":[0.003988181,0.0005339935,0.001300332,0.002622323,0.0004074558,0.000527011,0.001136107,0.001277527,0.01358423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001277944,"about_ca_system_score_gemma":0.001174634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04796212,"about_ca_topic_score_gemma":0.100327,"domain_scores_codex":[0.9995248,0.00008327838,0.0000333617,0.0001541685,0.0001150709,0.00008926036],"domain_scores_gemma":[0.999132,0.0002980499,0.00008454482,0.0002061366,0.0001935691,0.00008570929],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004255512,0.000149821,0.008061866,0.001182594,0.0003138848,0.0003205803,0.0001119077,0.004616928,0.001333812,0.0009062052,0.9609974,0.02157932],"study_design_scores_gemma":[0.001611827,0.0003062389,0.1227922,0.001392549,0.0007027526,0.002051862,0.0006000592,0.03270349,0.004682306,0.009546808,0.8234013,0.0002085963],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.009656595,0.0005878299,0.001139227,0.0001806783,0.0001211915,0.0000853088,0.9851034,0.001212107,0.001913599],"genre_scores_gemma":[0.008061125,0.0001644717,0.001053359,0.0000455249,0.00001926881,0.0001663173,0.989346,0.00008072572,0.001063318],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04796212,"threshold_uncertainty_score":0.09536594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01362894596169317,"score_gpt":0.2442111952675958,"score_spread":0.2305822493059027,"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."}}