{"id":"W4390120235","doi":"10.1109/tim.2023.3345909","title":"Multibin Breathing Pattern Estimation by Radar Fusion for Enhanced Driver Monitoring","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Breathing; Radar; Computer science; Real-time computing; Respiratory monitoring; Apnea; Remote sensing; Simulation; Artificial intelligence; Telecommunications; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001940174,0.0001696511,0.0001175253,0.0001641882,0.0002350424,0.00005849798,0.00004881661,0.0000597231,0.000009407154],"category_scores_gemma":[0.000004540872,0.0001884889,0.00005074267,0.000172427,0.00001393102,0.0002771046,9.01025e-7,0.0001030275,0.00002222561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003125013,"about_ca_system_score_gemma":0.00001031122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002988077,"about_ca_topic_score_gemma":0.000006649505,"domain_scores_codex":[0.9989605,0.0000197434,0.0002294417,0.0002090651,0.0003657079,0.0002155378],"domain_scores_gemma":[0.9996817,0.00003969586,0.00003482153,0.00009885638,0.00006377845,0.00008111087],"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":[0.00001065777,0.00001853779,0.00005186935,0.00006776879,0.00003359316,3.607522e-7,0.0005258216,0.01600073,0.6971048,6.449058e-7,0.00005489579,0.2861304],"study_design_scores_gemma":[0.001135351,0.00007076194,0.0003586973,0.0001641514,0.00003292038,0.000001048065,0.0003828592,0.01162523,0.9859545,0.00002322089,0.00006512652,0.0001860878],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.329081,0.00001900051,0.6689463,0.000028941,0.001215231,0.0003734801,0.00002338748,0.0002840016,0.00002871745],"genre_scores_gemma":[0.9966751,0.000121132,0.00280228,0.0000131666,0.00005624663,0.0002504115,0.00001397529,0.00003811112,0.00002952307],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6675942,"threshold_uncertainty_score":0.7686353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02964914563176,"score_gpt":0.2528306000357293,"score_spread":0.2231814544039693,"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."}}