{"id":"W4297098274","doi":"10.1109/cogsima54611.2022.9903277","title":"Sensors-Enabled Human State Monitoring System for Tactical Settings","year":2022,"lang":"en","type":"article","venue":"","topic":"Healthcare Technology and Patient Monitoring","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thales (Canada)","funders":"","keywords":"Computer science; State (computer science); Systems engineering; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005973211,0.0007293591,0.0006904937,0.0007637216,0.0003152213,0.00083486,0.0008457039,0.0006755299,0.008750375],"category_scores_gemma":[0.001113911,0.0002756142,0.0002621578,0.0004379874,0.0001742886,0.00091158,0.001007436,0.0005909002,0.002593593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002786642,"about_ca_system_score_gemma":0.0004821104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000941603,"about_ca_topic_score_gemma":0.00179637,"domain_scores_codex":[0.9995773,0.00007492387,0.00003637061,0.0001404078,0.00013919,0.00003183474],"domain_scores_gemma":[0.9995607,0.00009294031,0.0000506771,0.00007409022,0.0001551389,0.00006649351],"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":[0.002681049,0.001606814,0.03319689,0.001706696,0.0004646274,0.001390987,0.002170982,0.01699978,0.1669569,0.007956862,0.07784407,0.6870244],"study_design_scores_gemma":[0.0004732312,0.003454905,0.1039094,0.000677349,0.0005226112,0.003192971,0.001104663,0.5808642,0.1419096,0.01386315,0.1495675,0.00046046],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1925616,0.001808124,0.7108998,0.001540075,0.001077458,0.001737799,0.007409044,0.05501874,0.0279473],"genre_scores_gemma":[0.8606963,0.0007056654,0.1194258,0.0009916665,0.0001881212,0.00123787,0.002739365,0.0004030173,0.01361244],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008750375,"threshold_uncertainty_score":0.02927291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04539541613561656,"score_gpt":0.3458907629613872,"score_spread":0.3004953468257707,"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."}}