{"id":"W5949954","doi":"10.14236/ewic/vocs2008.17","title":"Spontaneous Pain Expression Recognition in Video Sequences","year":2008,"lang":"en","type":"article","venue":"Electronic workshops in computing","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Canadian Institutes of Health Research","keywords":"Facial expression; Categorization; Expression (computer science); Computer science; Context (archaeology); Artificial intelligence; Facial expression recognition; Pattern recognition (psychology); Psychology; Speech recognition; Facial recognition system","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":[0.0004312336,0.0003309412,0.0002839177,0.0005618795,0.00008692441,0.0002908669,0.0002655233,0.000369014,0.002120277],"category_scores_gemma":[0.002909753,0.000085138,0.0001422663,0.0002927157,0.0001187461,0.0003461714,0.0001920873,0.0001842778,0.0006320706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009764508,"about_ca_system_score_gemma":0.00008752921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003873413,"about_ca_topic_score_gemma":0.0005229186,"domain_scores_codex":[0.9997059,0.00009056713,0.00001529602,0.00005179223,0.0001060667,0.00003041487],"domain_scores_gemma":[0.9990377,0.0004026367,0.0001581821,0.00006871545,0.0002798986,0.00005285677],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001496247,0.0001997029,0.01655776,0.0005507103,0.0000845185,0.0003734122,0.0002998701,0.003364842,0.5571516,0.0004738767,0.001841804,0.4176057],"study_design_scores_gemma":[0.0001083799,0.00318785,0.4492487,0.0001335952,0.0001661866,0.003588913,0.0007526538,0.1748572,0.3581384,0.002015242,0.007686665,0.0001162476],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7491032,0.0007818663,0.2434402,0.0001071089,0.0001134088,0.0003717176,0.001355759,0.001034186,0.003692498],"genre_scores_gemma":[0.8938096,0.0005657562,0.1015027,0.00006597423,0.00009657197,0.0002463795,0.001554324,0.00006846763,0.002090332],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002120277,"threshold_uncertainty_score":0.007093072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0311726573683697,"score_gpt":0.2926191938378953,"score_spread":0.2614465364695255,"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."}}