{"id":"W2101288631","doi":"10.1109/tbme.2010.2097261","title":"Adaptive Sleep–Wake Discrimination for Wearable Devices","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"École Polytechnique Fédérale de Lausanne; European Commission","keywords":"Computer science; Classifier (UML); Accelerometer; Wearable computer; Artificial intelligence; Machine learning; Statistical classification; Wake; Speech recognition; Adaptation (eye); Pattern recognition (psychology); Engineering; Psychology","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001388407,0.0002455529,0.0001937793,0.0002915673,0.000109498,0.00004332227,0.0001797959,0.0002006444,0.00005118006],"category_scores_gemma":[0.00001896523,0.0002529611,0.0001203851,0.000335192,0.00004825302,0.0002523049,0.000001254556,0.0005160469,0.00004198264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009076623,"about_ca_system_score_gemma":0.00001532296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001107002,"about_ca_topic_score_gemma":0.00002409709,"domain_scores_codex":[0.998826,0.000005577826,0.0002510318,0.0002407512,0.0002662886,0.0004104094],"domain_scores_gemma":[0.9992877,0.0002174827,0.00001943292,0.0001982569,0.00004697758,0.0002302121],"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":[0.00001751662,0.00009187594,0.000005244564,0.0001708872,0.000108727,0.000005744406,0.0001569376,0.1289097,0.8120529,0.0001673239,0.00008186761,0.05823129],"study_design_scores_gemma":[0.0008543857,0.0002129201,0.0001546543,0.0001839971,0.00007545312,0.00001783731,0.00009460122,0.4382604,0.5524401,0.00009320192,0.006989225,0.0006232003],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03588853,0.00004859944,0.9583297,0.00006189763,0.004476468,0.0002844597,0.00005227519,0.0006676686,0.0001903831],"genre_scores_gemma":[0.9817635,0.00002232721,0.01745,0.00001363733,0.0003943256,0.0002236126,0.00000575076,0.00008081726,0.00004599628],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.945875,"threshold_uncertainty_score":0.9999923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01128701042906269,"score_gpt":0.2203317846801865,"score_spread":0.2090447742511238,"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."}}