{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001848756,0.001061831,0.0009169753,0.001645405,0.0003435044,0.0007099494,0.001056294,0.0008127398,0.0005133352],"category_scores_gemma":[0.003708902,0.0004243364,0.0008736061,0.0009905952,0.0003855564,0.0008599369,0.001054186,0.0006632939,0.0003909552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004609055,"about_ca_system_score_gemma":0.0007106176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002866382,"about_ca_topic_score_gemma":0.004008996,"domain_scores_codex":[0.9990385,0.0002242071,0.00006706061,0.0003832672,0.0002084288,0.00007860814],"domain_scores_gemma":[0.998704,0.0004345434,0.0002137447,0.0001941138,0.0003842608,0.00006935251],"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":[0.0003398221,0.0002315748,0.008294737,0.0001215072,0.0003051414,0.0001679399,0.0002214548,0.2201222,0.0506927,0.002451407,0.001834249,0.7152172],"study_design_scores_gemma":[0.00001187342,0.00008239689,0.002848967,0.000008661385,0.00004179544,0.0000794872,0.00002533045,0.986701,0.008594749,0.0009352325,0.0006545659,0.00001600243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05808373,0.0003405044,0.9397815,0.00009578362,0.00004381674,0.00004999287,0.00006189985,0.001128234,0.0004145041],"genre_scores_gemma":[0.5202424,0.0001603353,0.4769445,0.0001080043,0.00009102863,0.00007480344,0.000443522,0.0002268423,0.001708591],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002866382,"threshold_uncertainty_score":0.009777308,"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."}}