{"id":"W3128352929","doi":"10.1109/access.2021.3057578","title":"Intelligent Stress Monitoring Assistant for First Responders","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère de la Défense Nationale","keywords":"Computer science; Stress (linguistics)","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":[0.0004665493,0.0008492853,0.0005276426,0.0003440434,0.0003064563,0.0006432999,0.0009385805,0.000672749,0.004977558],"category_scores_gemma":[0.001268216,0.0002055802,0.0002944444,0.0001407159,0.0001424272,0.0006588622,0.0007041821,0.0006230619,0.002311517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002895324,"about_ca_system_score_gemma":0.0004406899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006411038,"about_ca_topic_score_gemma":0.0007565093,"domain_scores_codex":[0.9997284,0.00005931797,0.0000191523,0.00008770424,0.00006990465,0.0000354441],"domain_scores_gemma":[0.9994554,0.000164362,0.00007013667,0.00008017028,0.0001554005,0.00007449936],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002734218,0.0006008865,0.01270887,0.0007481268,0.0001166201,0.001194035,0.0009246608,0.01248677,0.141364,0.003564519,0.03755782,0.7859996],"study_design_scores_gemma":[0.0006489441,0.002621163,0.02617334,0.0002842638,0.0003703259,0.004318995,0.001150399,0.6339029,0.178333,0.008632233,0.1433248,0.0002397138],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.185378,0.001824945,0.754958,0.002336158,0.0009798438,0.0006305476,0.001483255,0.03768611,0.01472313],"genre_scores_gemma":[0.7656909,0.0006123373,0.2182713,0.0009696991,0.0003791921,0.0003922365,0.001061715,0.0001636551,0.01245909],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004977558,"threshold_uncertainty_score":0.01665157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05219982678630999,"score_gpt":0.3055826011091901,"score_spread":0.25338277432288,"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."}}