{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00131128,0.0006792409,0.0007282666,0.001581286,0.0003604424,0.0007531647,0.0009232572,0.001136092,0.0008263875],"category_scores_gemma":[0.003046704,0.0002137744,0.0006758998,0.0008411185,0.0002377221,0.0006665969,0.000504816,0.0009923015,0.0004930391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003496,"about_ca_system_score_gemma":0.0005623206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002857315,"about_ca_topic_score_gemma":0.002577683,"domain_scores_codex":[0.9995568,0.0001212533,0.00006507154,0.0001290987,0.00007820391,0.00004952885],"domain_scores_gemma":[0.9982479,0.001220499,0.0001108825,0.00008215152,0.0002857041,0.0000528314],"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":[0.0003760386,0.0004567477,0.0211443,0.00008535128,0.000170338,0.0002439491,0.0000843105,0.05628039,0.01505638,0.001835101,0.002468232,0.9017989],"study_design_scores_gemma":[0.00002494085,0.0001606614,0.008397906,0.00002101597,0.00006263125,0.0002651553,0.00004780646,0.981496,0.004671209,0.004021488,0.0008029877,0.00002817111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1251948,0.001833529,0.8683717,0.0007038423,0.0001722512,0.0001229141,0.000619256,0.001146643,0.001835075],"genre_scores_gemma":[0.684715,0.0006387657,0.3108054,0.0002219229,0.0002355523,0.0001999661,0.0008200073,0.00003612683,0.002327303],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002857315,"threshold_uncertainty_score":0.006934822,"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."}}