{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006858495,0.001137054,0.0005377777,0.001675913,0.0001302166,0.0009972194,0.0004395194,0.0006642627,0.00160332],"category_scores_gemma":[0.004244603,0.0001733387,0.0005383563,0.000929771,0.0002050829,0.0008398012,0.0005539493,0.0007415031,0.001344699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001918876,"about_ca_system_score_gemma":0.0001438999,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001008293,"about_ca_topic_score_gemma":0.001677686,"domain_scores_codex":[0.9994332,0.0001254584,0.00003915027,0.0002077658,0.0001384567,0.00005584033],"domain_scores_gemma":[0.9985396,0.0006028585,0.0002026421,0.0001998981,0.0003976856,0.00005738626],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001352817,0.001088809,0.1078449,0.001021913,0.0004025584,0.0003832963,0.0002968619,0.03496786,0.09044755,0.001438742,0.01334261,0.7474121],"study_design_scores_gemma":[0.0000582806,0.0007090213,0.3078276,0.000204693,0.0001801133,0.0007074114,0.0005105993,0.6347193,0.04144571,0.005785964,0.007722918,0.0001284278],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5003937,0.003048559,0.4675867,0.0007041469,0.0007562679,0.0003719697,0.0127723,0.004807959,0.009558441],"genre_scores_gemma":[0.9174788,0.0008455809,0.07192399,0.0001693718,0.0002000934,0.0002338288,0.007404224,0.0001063052,0.001637839],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001675913,"threshold_uncertainty_score":0.005363584,"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."}}