{"id":"W2779957450","doi":"","title":"Identifying correlation between facial expression and heart rate and skin conductance with iMotions biometric platform","year":2017,"lang":"en","type":"article","venue":"","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Sadness; Skin conductance; Facial expression; Anger; Disgust; Heart rate variability; Biometrics; Stimulus (psychology); Heart rate; Psychology; Psychophysiology; Contempt; Audiology; Cognitive psychology; Communication; Artificial intelligence; Computer science; Neuroscience; Social psychology; Medicine; Internal medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001849522,0.00009179204,0.000121428,0.0002200067,0.0005399962,0.00014184,0.00004056578,0.00009960749,0.000331666],"category_scores_gemma":[0.00003747246,0.00007265491,0.00001602355,0.0001007379,0.0001142208,0.0004492688,0.00003459749,0.0001073877,0.00007131841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008899365,"about_ca_system_score_gemma":0.000006687435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007998141,"about_ca_topic_score_gemma":0.00004225969,"domain_scores_codex":[0.9993807,0.00003282448,0.0001356243,0.0002364646,0.00007932063,0.0001350404],"domain_scores_gemma":[0.9995201,0.00005922582,0.0001229269,0.0001744996,0.00004538162,0.000077909],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002165775,0.0002331184,0.7034008,0.00009788758,0.0001770399,0.00002064206,0.004592951,0.000001384456,0.04539838,0.006950744,0.004862288,0.2340482],"study_design_scores_gemma":[0.001232188,0.00008446472,0.9944484,0.0000467353,0.00002359577,0.00002585434,0.0004228518,0.00002386154,0.002087306,0.0008129541,0.0006723127,0.0001195412],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9748424,0.0000614504,0.01132205,0.0001929747,0.0003361401,0.000199705,0.00001585193,0.00004527779,0.01298415],"genre_scores_gemma":[0.9967876,0.0000214597,0.0008994766,0.00004976749,0.00009237698,0.00001028826,0.00003474372,0.000009546448,0.002094723],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2910475,"threshold_uncertainty_score":0.4153269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1035749526435175,"score_gpt":0.3620560583710472,"score_spread":0.2584811057275297,"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."}}