{"id":"W2157507557","doi":"10.1016/j.clinph.2011.04.002","title":"A machine learning approach for distinguishing age of infants using auditory evoked potentials","year":2011,"lang":"en","type":"article","venue":"Clinical Neurophysiology","topic":"Neonatal and fetal brain pathology","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Electroencephalography; Stimulus (psychology); A priori and a posteriori; Psychology; Artificial intelligence; Cluster analysis; Classifier (UML); Audiology; Speech recognition; Pattern recognition (psychology); Event-related potential; Computer science; Machine learning; Cognitive 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003487721,0.0001685178,0.0008301804,0.00006972455,0.00008118766,0.000002496617,0.0001672704,0.0002146256,0.00003626951],"category_scores_gemma":[0.0037916,0.0001371325,0.0003248175,0.0001007026,0.0005429415,0.00003532522,0.0001870751,0.000500943,0.000006974194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006653013,"about_ca_system_score_gemma":0.00004080141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002845226,"about_ca_topic_score_gemma":3.033964e-7,"domain_scores_codex":[0.9980174,0.0003353555,0.0007655987,0.0004879249,0.00008822534,0.000305452],"domain_scores_gemma":[0.9985549,0.0005854385,0.0003281257,0.0002898825,0.0001097268,0.0001319095],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.004641807,0.001336484,0.0178316,0.0005275882,0.0001968778,0.0005450104,0.0003260132,0.0001025039,0.9598753,0.0005200071,0.00005218143,0.01404464],"study_design_scores_gemma":[0.0108542,0.01460544,0.9097478,0.0002036467,0.0008895447,0.0005702128,0.0001149718,0.04683711,0.003887748,0.006589551,0.00480845,0.0008913336],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.989839,0.00009155315,0.006767625,0.00001808519,0.0009430996,0.0004198482,0.0000185071,0.00006354474,0.001838789],"genre_scores_gemma":[0.9809452,0.00003208634,0.0174499,0.0004751087,0.0007147507,0.00001097752,0.00008746048,0.00003681565,0.000247756],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9559876,"threshold_uncertainty_score":0.5592103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1210967048022045,"score_gpt":0.3529271292396485,"score_spread":0.2318304244374439,"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."}}