{"id":"W4311562785","doi":"10.1155/2022/4610747","title":"Physiological Status Prediction Based on a Novel Hybrid Intelligent Scheme","year":2022,"lang":"en","type":"article","venue":"Computational Intelligence and Neuroscience","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China; Shihezi University; York University; Case Western Reserve University; Johns Hopkins University; University of California, Davis; University of Arizona; University of Minnesota; University of Washington; National Heart, Lung, and Blood Institute; Boston University; New York University","keywords":"Computer science; Feature selection; Support vector machine; Artificial intelligence; Machine learning; Particle swarm optimization; Mutual information; Scheme (mathematics); Classifier (UML); Data mining; Linear discriminant analysis; Pattern recognition (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":[0.0008671845,0.0007521277,0.0008263934,0.001106824,0.0005549962,0.00092756,0.001020903,0.000846235,0.001210797],"category_scores_gemma":[0.002241893,0.0002789438,0.000507911,0.0006029259,0.0004073972,0.001265489,0.0009502771,0.0004919026,0.0003396505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004366417,"about_ca_system_score_gemma":0.0004874994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002215357,"about_ca_topic_score_gemma":0.001884499,"domain_scores_codex":[0.9993485,0.00008527962,0.00008796706,0.0002046915,0.0002024115,0.00007124265],"domain_scores_gemma":[0.9992931,0.0001923159,0.0001202953,0.00008498636,0.0002569674,0.00005232184],"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":[0.0007953876,0.0005030434,0.01474503,0.0001398199,0.000220472,0.0003872242,0.0003564617,0.2699095,0.046731,0.005282045,0.003024688,0.6579053],"study_design_scores_gemma":[0.000020568,0.0001104562,0.002096242,0.000008865719,0.00004199442,0.00008883243,0.00001255179,0.9926245,0.003342134,0.00109639,0.0005354926,0.00002194866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1164199,0.0004734896,0.8777891,0.0003518852,0.0001236941,0.0001772251,0.000126735,0.001349622,0.003188482],"genre_scores_gemma":[0.9193233,0.0001314113,0.07844798,0.0001649321,0.00005771826,0.0001259661,0.0001319158,0.0000249959,0.00159173],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002215357,"threshold_uncertainty_score":0.00458616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1053003455521949,"score_gpt":0.3143482400364252,"score_spread":0.2090478944842303,"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."}}