{"id":"W1983457024","doi":"10.1007/s11682-014-9292-1","title":"Fusion analysis of functional MRI data for classification of individuals based on patterns of activation","year":2014,"lang":"en","type":"article","venue":"Brain Imaging and Behavior","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University; University of British Columbia","funders":"Canadian Institutes of Health Research","keywords":"Support vector machine; Pattern recognition (psychology); Artificial intelligence; Contrast (vision); Computer science; Receiver operating characteristic; Independent component analysis; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000556156,0.00008568897,0.0002103965,0.0003727526,0.00008125621,0.000009697331,0.0001305749,0.00002641212,0.0000173277],"category_scores_gemma":[0.00346351,0.0000793985,0.00006174834,0.0003354069,0.00009072263,0.0001425715,0.00006517114,0.00004617691,2.505695e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001263542,"about_ca_system_score_gemma":0.00002120673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002591695,"about_ca_topic_score_gemma":0.000006665936,"domain_scores_codex":[0.9988975,0.0001014901,0.0002501176,0.0003687989,0.0002980938,0.00008398489],"domain_scores_gemma":[0.9948421,0.004352362,0.0002898387,0.0003886387,0.0001073253,0.00001978087],"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":[0.0000606256,0.0003261816,0.3391922,0.00005350301,0.00002202509,4.725544e-8,0.00005469303,0.0003174605,0.6473329,0.0004505858,0.001017777,0.01117207],"study_design_scores_gemma":[0.0004672211,0.00007547284,0.8445273,0.00003906307,0.0003151167,2.666734e-7,0.00006875927,0.07561455,0.07847487,0.00003176821,0.0003151266,0.00007050367],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9177672,0.000003273396,0.07787733,0.003166243,0.00008005486,0.000223874,0.0008238392,0.00001525088,0.0000429321],"genre_scores_gemma":[0.9988621,0.000001543126,0.0003829748,0.0004007691,0.00002439425,0.00003246966,0.0002561719,0.000007507124,0.00003205514],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.568858,"threshold_uncertainty_score":0.4146396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0913423164710099,"score_gpt":0.3218558208805846,"score_spread":0.2305135044095747,"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."}}