{"id":"W2897309261","doi":"10.1007/978-3-030-02628-8_4","title":"Finding Effective Ways to (Machine) Learn fMRI-Based Classifiers from Multi-site Data","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Classifier (UML); Artificial intelligence; Machine learning; Normalization (sociology); Functional magnetic resonance imaging; Training set; Data mining; Pattern recognition (psychology)","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002767142,0.001845698,0.002118096,0.001520657,0.0004399486,0.00236173,0.00240774,0.002819395,0.002961961],"category_scores_gemma":[0.01073756,0.001053567,0.001852785,0.001846199,0.0008793045,0.003901911,0.001800209,0.00354723,0.002194478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004976684,"about_ca_system_score_gemma":0.00101572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001402759,"about_ca_topic_score_gemma":0.002492379,"domain_scores_codex":[0.9988378,0.0003466877,0.000116481,0.0003664648,0.000240566,0.00009214455],"domain_scores_gemma":[0.9956617,0.003124791,0.0002501718,0.0004480145,0.0004131726,0.0001021377],"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":[0.000159908,0.00017447,0.0016199,0.0005273847,0.0003827709,0.000153135,0.000135123,0.07693078,0.01430792,0.01385882,0.01043407,0.8813157],"study_design_scores_gemma":[0.00002714171,0.0001074266,0.0006345692,0.00007790248,0.0001119006,0.0002083697,0.00007238432,0.9259748,0.00870066,0.06037276,0.003675686,0.0000363163],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004178577,0.0008716251,0.9930744,0.0003884772,0.00008559594,0.0000375232,0.000164476,0.000818859,0.0003804732],"genre_scores_gemma":[0.08047631,0.001618248,0.9125143,0.0003814991,0.0004003908,0.0003095935,0.001140048,0.0003106709,0.002849019],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002961961,"threshold_uncertainty_score":0.01463425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0909407209251088,"score_gpt":0.2983109831498024,"score_spread":0.2073702622246936,"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."}}