{"id":"W2941172668","doi":"10.1007/978-3-030-21741-9_27","title":"Cough Detection Using Hidden Markov Models","year":2019,"lang":"en","type":"preprint","venue":"Lecture notes in computer science","topic":"Respiratory and Cough-Related Research","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Univariate; Hidden Markov model; Multivariate statistics; Context (archaeology); Medicine; Multivariate analysis; Public health; Receiver operating characteristic; Artificial intelligence; Computer science; Intensive care medicine; Machine learning; Pathology; Geography","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":[0.001090651,0.0003031373,0.0004584579,0.0006237265,0.0001733972,0.0002133576,0.0007165454,0.0005092889,0.00002321566],"category_scores_gemma":[0.0001274755,0.0002558625,0.0001248224,0.001094718,0.0003890535,0.0002349627,0.001152527,0.001772828,0.0000179123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005142357,"about_ca_system_score_gemma":0.001294133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001480545,"about_ca_topic_score_gemma":0.00002274665,"domain_scores_codex":[0.9967698,0.0001168492,0.0003609571,0.001094133,0.001067658,0.0005905834],"domain_scores_gemma":[0.9981741,0.0001776372,0.0001329296,0.001039303,0.0003135494,0.0001624281],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007521245,0.00004786696,0.0007291307,0.00018748,0.00001764878,0.00007937307,0.0004005375,0.6317967,0.005389513,0.000004769031,0.00000298376,0.3612688],"study_design_scores_gemma":[0.000405261,0.0001543827,0.001010479,0.0006441943,0.00002199123,0.0000945467,0.000001041445,0.976723,0.01545488,0.005208415,0.00002537896,0.0002564106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3825996,0.0004023586,0.6149238,0.0001989498,0.001234681,0.0004907757,0.00000165652,0.00004788412,0.000100338],"genre_scores_gemma":[0.9478939,0.00003660452,0.05090591,0.0006202884,0.0004960123,0.00001147067,0.000003150561,0.00002487764,0.00000781848],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5652943,"threshold_uncertainty_score":0.9999893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05062887878392913,"score_gpt":0.3266154526766167,"score_spread":0.2759865738926876,"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."}}