{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002215174,0.0002169992,0.0001632132,0.0002076545,0.0008446922,0.0001461956,0.0005418425,0.00002149482,0.0001636869],"category_scores_gemma":[0.0003829051,0.0001960917,0.00007103882,0.0005768538,0.0004475182,0.0002076984,0.0002878986,0.0004020279,0.00001783898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007055054,"about_ca_system_score_gemma":0.00008745656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004558181,"about_ca_topic_score_gemma":1.127835e-7,"domain_scores_codex":[0.9973129,0.000155924,0.0003341612,0.000974676,0.0007944596,0.0004278689],"domain_scores_gemma":[0.9988368,0.0006044052,0.000126004,0.0002080787,0.00004972689,0.0001749452],"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":[0.00005773796,0.0003799586,0.0001478668,0.000006540036,6.390095e-7,0.00001937839,0.00009935447,0.9109839,0.07848163,0.006544126,0.0003531309,0.002925772],"study_design_scores_gemma":[0.0001035,0.0009935541,0.001397631,0.00001202242,0.000002034952,0.0000640975,0.00006755025,0.897025,0.09442782,0.002061192,0.003658938,0.0001865905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3972403,0.00003711006,0.5988216,0.0009668261,0.001361802,0.0004235732,0.0002574356,0.0002276785,0.0006636886],"genre_scores_gemma":[0.9891002,0.00001415017,0.001164738,0.009479145,0.00005196618,0.0000552606,0.000008626961,0.00001247114,0.0001134393],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5976569,"threshold_uncertainty_score":0.7996387,"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."}}