{"id":"W4405247999","doi":"10.1145/3705722","title":"WiSleep: Smartphone-driven Sleep Population Monitoring with Unsupervised Learning","year":2024,"lang":"en","type":"article","venue":"ACM Journal on Computing and Sustainable Societies","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"U.S. Army; National Science Foundation","keywords":"Wearable computer; Computer science; Sleep (system call); Personalization; Software deployment; Population; Machine learning; Wearable technology; Artificial intelligence; Supervised learning; Deep learning; Human–computer interaction; Medicine; World Wide Web; Embedded system; Artificial neural network","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.001919931,0.0001355945,0.0001870829,0.0001568945,0.003751485,0.001001107,0.0001911292,0.00009165448,0.00003717703],"category_scores_gemma":[0.0004029739,0.0001152158,0.0001077485,0.000481223,0.0001700535,0.0003078124,0.00006017898,0.0006203054,0.00000504249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003291936,"about_ca_system_score_gemma":0.0001922044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001341298,"about_ca_topic_score_gemma":0.00004943054,"domain_scores_codex":[0.9982302,0.0003806697,0.0002273081,0.0002325033,0.0004576959,0.0004716761],"domain_scores_gemma":[0.9988731,0.0005193498,0.00008162894,0.000118045,0.0002742739,0.0001335693],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.00008677963,0.000123337,0.3409598,0.0006413764,0.0006037952,0.0002742018,0.2353849,0.2144414,0.00003434927,0.02264298,0.001281604,0.1835255],"study_design_scores_gemma":[0.0008236567,0.0006293458,0.04449377,0.001047884,0.0002875589,0.00002842885,0.8285754,0.08058815,0.00004329683,0.01672599,0.02594301,0.0008135178],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915469,0.001476122,0.003073064,0.002453524,0.0002174659,0.00009357685,2.117814e-7,0.0001686763,0.0009704034],"genre_scores_gemma":[0.9949563,0.0002866765,0.0004650438,0.00006029823,0.0008419818,0.000002154107,0.000002905221,0.00001661729,0.003368062],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5931905,"threshold_uncertainty_score":0.9975455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0128580652044561,"score_gpt":0.2901386494241391,"score_spread":0.277280584219683,"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."}}