{"id":"W2008371765","doi":"10.1016/j.jneumeth.2003.09.025","title":"Design and validation of a computer-based sleep-scoring algorithm","year":2003,"lang":"en","type":"article","venue":"Journal of Neuroscience Methods","topic":"Sleep and Wakefulness Research","field":"Neuroscience","cited_by":109,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Canada Research Chairs","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sleep (system call); Computer science; Algorithm; Artificial intelligence; Machine learning; Programming language","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":[0.007174106,0.00077015,0.0008937194,0.0009271465,0.0007609468,0.001229936,0.001693316,0.001292431,0.004302907],"category_scores_gemma":[0.01446618,0.0005777582,0.0005161412,0.0005596149,0.0005879965,0.0008058459,0.001071946,0.0008327993,0.001215318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007953353,"about_ca_system_score_gemma":0.002617993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002763777,"about_ca_topic_score_gemma":0.00211058,"domain_scores_codex":[0.996917,0.001248923,0.0003004084,0.0008529436,0.0005197953,0.0001608316],"domain_scores_gemma":[0.9886431,0.005025466,0.0005253319,0.000803899,0.004633462,0.0003687342],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.007117718,0.00194537,0.03009109,0.0003883995,0.0003612714,0.000138698,0.0004371377,0.1070874,0.06388608,0.004503359,0.002834242,0.7812091],"study_design_scores_gemma":[0.0007935214,0.001663044,0.009311924,0.00002276251,0.0001469595,0.0001750275,0.00007236168,0.9598067,0.02440536,0.001518754,0.002036768,0.00004675677],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04615521,0.00005165188,0.9496632,0.00006367472,0.00005980032,0.002045831,0.0001000136,0.001433601,0.0004270449],"genre_scores_gemma":[0.2127614,0.00003585668,0.7823282,0.00008195475,0.0000295723,0.003429692,0.0003071646,0.0001477682,0.0008783356],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007174106,"threshold_uncertainty_score":0.03794074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1701250827907076,"score_gpt":0.4195762353688144,"score_spread":0.2494511525781068,"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."}}