{"id":"W4391770869","doi":"10.1088/1361-6579/ad290b","title":"Unsupervised ensembling of multiple software sensors with phase synchronization: a robust approach for electrocardiogram-derived respiration","year":2024,"lang":"en","type":"article","venue":"Physiological Measurement","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"York University; University of Washington; Johns Hopkins University; National Heart, Lung, and Blood Institute; University of California, Davis; University of Minnesota; Case Western Reserve University; National Science Foundation","keywords":"Synchronization (alternating current); Computer science; Phase synchronization; sync; Sensitivity (control systems); SIGNAL (programming language); Pattern recognition (psychology); Artificial intelligence; Electronic engineering; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002572848,0.0002321647,0.0002997905,0.00009128696,0.00007640542,0.00004665279,0.0001089322,0.00008570994,0.000004646863],"category_scores_gemma":[0.0001577782,0.0001777041,0.0001276057,0.0003861456,0.00003890684,0.0001174457,0.00001750795,0.0001391184,0.000002202751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002074825,"about_ca_system_score_gemma":0.00003344379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003759322,"about_ca_topic_score_gemma":0.000001022139,"domain_scores_codex":[0.998625,0.00005255669,0.0002760924,0.0003490802,0.0003885878,0.0003087164],"domain_scores_gemma":[0.9993634,0.00009577597,0.00003178136,0.0001853667,0.000252479,0.00007123721],"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":[0.0000881214,0.0000776915,0.00008105137,0.0004519754,0.0001473098,0.000002094381,0.00006660433,0.2720912,0.7229171,0.00001642532,0.00003841338,0.00402204],"study_design_scores_gemma":[0.001970401,0.001264345,0.0003233832,0.0003509969,0.0001297926,0.000005215357,0.0001095708,0.263931,0.7311805,0.0001508927,0.0000939469,0.0004899972],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2556562,0.0009323805,0.7421525,0.000005415521,0.0001043798,0.0006732229,0.00001164652,0.000427171,0.00003721645],"genre_scores_gemma":[0.9627166,0.00002672412,0.03656741,0.000005725247,0.0002833868,0.0003015658,0.00004966192,0.00004784482,0.000001043348],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7070605,"threshold_uncertainty_score":0.7246562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07797817041398718,"score_gpt":0.2516036784907967,"score_spread":0.1736255080768095,"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."}}