{"id":"W2886606283","doi":"10.1016/j.jneumeth.2018.08.014","title":"A sleep spindle detection algorithm that emulates human expert spindle scoring","year":2018,"lang":"en","type":"article","venue":"Journal of Neuroscience Methods","topic":"Sleep and Wakefulness Research","field":"Neuroscience","cited_by":135,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Sleep & Circadian Network; Université de Montréal; Hôpital du Sacré-Cœur de Montréal","funders":"National Center for Advancing Translational Sciences; National Center for Research Resources; National Heart, Lung, and Blood Institute; Canadian Institutes of Health Research; Pfizer; Eli Lilly and Company","keywords":"Computer science; Sleep (system call); Artificial intelligence; Pattern recognition (psychology); Machine learning; Algorithm; Operating system","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":[0.002709815,0.0002476342,0.000379284,0.0006727632,0.0009918227,0.0003796697,0.001187752,0.00009975446,0.00006511401],"category_scores_gemma":[0.003051119,0.0001968833,0.0002168956,0.001416359,0.0008232304,0.00120295,0.0002719722,0.0006572381,0.00001947257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001037411,"about_ca_system_score_gemma":0.00007292916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002238181,"about_ca_topic_score_gemma":0.000002253368,"domain_scores_codex":[0.995789,0.001056815,0.0005806406,0.0005965423,0.001257587,0.0007194438],"domain_scores_gemma":[0.9980304,0.000415758,0.0005029055,0.0004337859,0.0002568772,0.0003602581],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000212633,0.00008186798,0.000136059,0.000003903906,0.000001424441,0.00006318681,0.0002138734,0.00001687669,0.7546058,0.00002951388,0.00001279591,0.2448134],"study_design_scores_gemma":[0.0004259461,0.001277139,0.00414587,0.00004031021,0.000008787798,0.0008776538,0.00008736908,0.01500217,0.9726962,0.0008416041,0.004388122,0.0002088561],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5370172,0.00007929466,0.4579782,0.0002031444,0.003834847,0.0001697631,0.000001452006,0.00004944235,0.0006667066],"genre_scores_gemma":[0.9545515,0.00005155816,0.04342638,0.0007236161,0.001026168,0.000007997502,3.927615e-8,0.00003493488,0.0001777986],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4175343,"threshold_uncertainty_score":0.8028666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2017992252712866,"score_gpt":0.4718115871054169,"score_spread":0.2700123618341303,"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."}}