{"id":"W2083355945","doi":"10.1038/nmeth.2855","title":"Sleep-spindle detection: crowdsourcing and evaluating performance of experts, non-experts and automated methods","year":2014,"lang":"en","type":"article","venue":"Nature Methods","topic":"Sleep and Wakefulness Research","field":"Neuroscience","cited_by":380,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Center for Research Resources; National Center for Advancing Translational Sciences; National Institute of Neurological Disorders and Stroke; National Heart, Lung, and Blood Institute; Canadian Institutes of Health Research","keywords":"Crowdsourcing; Computer science; Polysomnography; Identification (biology); Sleep (system call); Population; Artificial intelligence; Electroencephalography; Gold standard (test); Machine learning; Data mining; Data science; Medicine; Psychology; Neuroscience; World Wide Web; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0476372112603679,"score_gpt":0.4604568082439071,"score_spread":0.4128195969835392,"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."}}