{"id":"W2977742585","doi":"10.1016/j.neuroimage.2019.116226","title":"Brain status modeling with non-negative projective dictionary learning","year":2019,"lang":"en","type":"article","venue":"NeuroImage","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; École de Technologie Supérieure; McGill University; Montreal Neurological Institute and Hospital","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Mental Health; National Aerospace Science Foundation of China; National Institutes of Health; Fonds de Recherche du Québec - Santé; National Natural Science Foundation of China; Fondation Brain Canada","keywords":"Discriminative model; Neuroimaging; Artificial intelligence; Computer science; Pattern recognition (psychology); Feature selection; Feature (linguistics); Psychology; Neuroscience","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005138367,0.0004578315,0.0003968955,0.0004328833,0.000133505,0.0004290593,0.000644675,0.0004406363,0.0007193554],"category_scores_gemma":[0.001798272,0.0002357689,0.0004602616,0.0003597975,0.0003810873,0.000589586,0.000500903,0.0006005212,0.0002578217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000276809,"about_ca_system_score_gemma":0.0003795359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003124872,"about_ca_topic_score_gemma":0.003710599,"domain_scores_codex":[0.9998012,0.00007166521,0.00001050728,0.00006471499,0.00002986971,0.00002205361],"domain_scores_gemma":[0.9995357,0.0002274009,0.0000710676,0.00005927351,0.00008041281,0.00002621302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001838662,0.0001029926,0.01151365,0.00009104772,0.0001287449,0.0002219864,0.000160688,0.7458235,0.01189826,0.0140395,0.002705148,0.2131306],"study_design_scores_gemma":[0.000003242186,0.00001935007,0.0004971721,0.000002118557,0.000004331107,0.00003041689,0.000006289356,0.9965217,0.000507749,0.002207278,0.0001969606,0.000003308283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08182756,0.0002418252,0.9164395,0.0001825201,0.00002776153,0.00003446928,0.0002379689,0.0002430124,0.0007653754],"genre_scores_gemma":[0.8547413,0.0003064684,0.1412273,0.0001261376,0.00004388097,0.0001106311,0.0007122839,0.00004931882,0.002682718],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003124872,"threshold_uncertainty_score":0.006213367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03745440563170813,"score_gpt":0.3286533286392739,"score_spread":0.2911989230075658,"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."}}