{"id":"W4380988651","doi":"10.3390/s23125613","title":"Prediction of Continuous Emotional Measures through Physiological and Visual Data","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Ottawa Mental Health Centre; University of Ottawa; Université du Québec en Outaouais","funders":"Canada Research Chairs","keywords":"Valence (chemistry); Arousal; Computer science; Machine learning; Artificial intelligence; Data pre-processing; Concordance correlation coefficient; Personalization; Feature selection; Preprocessor; Data mining; Psychology; Statistics","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":[0.0001693644,0.00006399553,0.0001145986,0.00004529476,0.00004373604,0.000005120437,0.00005719631,0.00008954394,0.0003007136],"category_scores_gemma":[0.00008418497,0.00005427575,0.00002361094,0.0001255489,0.00009860908,0.00005149937,0.00005073965,0.00007825756,0.0001879692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003545279,"about_ca_system_score_gemma":0.000005818581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002534386,"about_ca_topic_score_gemma":0.000003567219,"domain_scores_codex":[0.9992568,0.00011931,0.0001504752,0.0002348204,0.0001211544,0.0001174552],"domain_scores_gemma":[0.9996728,0.00005707146,0.00005305887,0.0001417602,0.00004813996,0.00002716274],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.001011196,0.002456195,0.06081426,0.0002593718,0.001288866,0.00014182,0.01290825,0.0002614223,0.1087501,0.02272505,0.5702965,0.219087],"study_design_scores_gemma":[0.001026532,0.0003092193,0.984665,0.00003369303,0.0000378998,0.00003993159,0.002164101,0.002082181,0.0004282132,0.001909973,0.007189111,0.0001140863],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9931236,0.00003858959,0.0001624918,0.0002148061,0.0006520584,0.0001050853,0.0001441694,0.0001311595,0.005428031],"genre_scores_gemma":[0.9979187,0.00006841015,0.00006324129,0.00007296808,0.000178019,0.000003093193,0.0005614962,0.000007412667,0.001126618],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9238508,"threshold_uncertainty_score":0.3292603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1940617333012068,"score_gpt":0.3718001890711568,"score_spread":0.17773845576995,"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."}}