{"id":"W3033598944","doi":"10.1007/s12021-020-09470-y","title":"Regularized Bagged Canonical Component Analysis for Multiclass Learning in Brain Imaging","year":2020,"lang":"en","type":"article","venue":"Neuroinformatics","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Institute on Aging; Canadian Institutes of Health Research","keywords":"Interpretability; Artificial intelligence; Computer science; Feature selection; Pattern recognition (psychology); Feature (linguistics); Multiclass classification; Projection (relational algebra); Neuroimaging; Machine learning; Multivariate statistics; Support vector machine; Algorithm; Psychology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007098448,0.001913434,0.002523356,0.00191173,0.001489852,0.002262945,0.003285578,0.002287616,0.003697252],"category_scores_gemma":[0.02006168,0.001046153,0.003187838,0.003029023,0.001648134,0.002126102,0.002558651,0.005071065,0.002592915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001474521,"about_ca_system_score_gemma":0.004957362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0177664,"about_ca_topic_score_gemma":0.02280262,"domain_scores_codex":[0.9962611,0.002129054,0.0002335969,0.0006411304,0.0004976926,0.000237338],"domain_scores_gemma":[0.9920455,0.004144345,0.0003936888,0.001647148,0.001566702,0.0002025087],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003652209,0.0004180204,0.001641796,0.000540843,0.0008560501,0.0001259774,0.0002599251,0.2854111,0.005952979,0.03210151,0.03017875,0.6421479],"study_design_scores_gemma":[0.00001581031,0.00002798685,0.0004511708,0.00003368469,0.00004514043,0.00003785978,0.00002089596,0.979707,0.001466862,0.01604789,0.002113814,0.00003183261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004254354,0.0007983573,0.9923688,0.0002443727,0.0001145381,0.0000793048,0.0002350844,0.001676603,0.0002287149],"genre_scores_gemma":[0.15034,0.001082322,0.8396263,0.0003829389,0.0002297895,0.0008419105,0.002282442,0.001296161,0.003918115],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0177664,"threshold_uncertainty_score":0.03754067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04983521678173803,"score_gpt":0.2758465876264823,"score_spread":0.2260113708447443,"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."}}