{"id":"W4214716485","doi":"10.5220/0010823300003123","title":"Enhancement of Physiological Stress Classification using Psychometric Features","year":2022,"lang":"en","type":"article","venue":"Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Stress (linguistics); Computer science; Artificial intelligence; Clinical psychology; Psychology","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":[0.0009614038,0.0007656289,0.0005258617,0.001009289,0.0001530295,0.0007232529,0.0003455515,0.0004928156,0.001900995],"category_scores_gemma":[0.002859444,0.0001514527,0.0004586879,0.0008608002,0.0001020197,0.0006126338,0.0006240517,0.0005151115,0.001270836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009843345,"about_ca_system_score_gemma":0.0002381104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008092807,"about_ca_topic_score_gemma":0.0009194687,"domain_scores_codex":[0.9996789,0.00007290567,0.00002336215,0.00006686673,0.0001065977,0.00005144276],"domain_scores_gemma":[0.9988875,0.0004200164,0.00008220164,0.0001204217,0.0004305498,0.00005933939],"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":[0.0007972707,0.0005603885,0.02884268,0.0001565397,0.0001057283,0.0001205111,0.00006834013,0.004782536,0.148842,0.0003995604,0.003239461,0.812085],"study_design_scores_gemma":[0.0001044789,0.001024732,0.2828043,0.00009504136,0.0003290922,0.0007503566,0.0002464243,0.5980167,0.1068806,0.002459087,0.007183347,0.000105937],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4209951,0.001242895,0.5669825,0.0004164548,0.0004057419,0.0002060767,0.001400244,0.003568522,0.004782548],"genre_scores_gemma":[0.9161835,0.0004432146,0.07922655,0.0001146004,0.0001747657,0.0001174143,0.001304973,0.0001547609,0.002280286],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001900995,"threshold_uncertainty_score":0.006359458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0727346568207097,"score_gpt":0.2979720068650785,"score_spread":0.2252373500443688,"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."}}