{"id":"W6968171644","doi":"10.5281/zenodo.15632715","title":"The Courtois Neuromod project: a deep, multi-domain fMRI dataset to build individual brain models","year":2025,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Institut Universitaire de Gériatrie de Montréal","funders":"","keywords":"Presentation (obstetrics); Cognition; Functional magnetic resonance imaging; Brain activity and meditation; Neuroimaging; Resource (disambiguation); Cognitive neuroscience; Brain mapping","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":[0.001119653,0.002082262,0.0008008407,0.00132903,0.0006244482,0.001436029,0.002392106,0.002037111,0.01723081],"category_scores_gemma":[0.003587446,0.0009760379,0.00161221,0.001069644,0.0004309232,0.0009968,0.001940396,0.001827163,0.009894582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009968707,"about_ca_system_score_gemma":0.001463226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02518356,"about_ca_topic_score_gemma":0.06778002,"domain_scores_codex":[0.9997031,0.00007497254,0.00001670563,0.0001050553,0.00006660714,0.00003357802],"domain_scores_gemma":[0.9994351,0.0001639795,0.00003001128,0.0002035232,0.00009740409,0.00006992381],"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":[0.0005683818,0.0001940552,0.002437388,0.0008692879,0.0005857954,0.0004514284,0.0001723325,0.0279438,0.01612405,0.003743033,0.8447688,0.1021418],"study_design_scores_gemma":[0.0008683985,0.0003283447,0.01790822,0.0005018777,0.000307071,0.002010983,0.000252268,0.2743165,0.01941733,0.05220194,0.6314948,0.0003922672],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.02894587,0.003503725,0.3603027,0.002442706,0.001112529,0.001039868,0.5267101,0.06494045,0.01100203],"genre_scores_gemma":[0.1023962,0.001495953,0.2165847,0.001325881,0.0002798016,0.003099022,0.6545968,0.008268717,0.01195298],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02518356,"threshold_uncertainty_score":0.05764282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08385403867846762,"score_gpt":0.3032066119313607,"score_spread":0.219352573252893,"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."}}