{"id":"W4237646711","doi":"10.22215/etd/2012-09526","title":"Context-aware algorithms for sleep apnea monitoring and sensor acceptance using unobtrusive pressure sensors arrays","year":2012,"lang":"en","type":"dissertation","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Canadian Heritage; Library and Archives Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sleep apnea; Polysomnography; Context (archaeology); Computer science; Obstructive sleep apnea; Medicine; Machine learning; Apnea; Algorithm; Artificial intelligence","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":[0.0003490369,0.0006685599,0.000550107,0.0005297665,0.0003224903,0.0006278564,0.0008976001,0.0006511928,0.00156049],"category_scores_gemma":[0.001872377,0.0003083422,0.0003459816,0.0003956977,0.0001532739,0.0008970803,0.0005755128,0.0005754794,0.0005128158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002213991,"about_ca_system_score_gemma":0.000377457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001163502,"about_ca_topic_score_gemma":0.003018745,"domain_scores_codex":[0.9996463,0.00007181741,0.00003099016,0.00008861806,0.0001222449,0.00004005204],"domain_scores_gemma":[0.9995609,0.000179392,0.00005641537,0.00004560355,0.0001329259,0.0000247977],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003666877,0.0002859094,0.003728264,0.00011386,0.00009034988,0.0001204,0.0001102192,0.06250088,0.05782062,0.002079464,0.002209926,0.8705735],"study_design_scores_gemma":[0.00003689336,0.0002897195,0.00402267,0.00002356826,0.00004285349,0.0001904337,0.00007896755,0.9622821,0.02908881,0.001909619,0.002011315,0.0000231472],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05520863,0.001454702,0.9397304,0.00015738,0.0001451979,0.00007745934,0.00005984419,0.001628067,0.001538255],"genre_scores_gemma":[0.6583609,0.0006339384,0.3375521,0.0001413565,0.0001356566,0.0002221299,0.0001429858,0.00007983856,0.002731213],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00156049,"threshold_uncertainty_score":0.005220294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04828034905830567,"score_gpt":0.3092399717575321,"score_spread":0.2609596226992264,"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."}}