{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001430204,0.0001725548,0.0002176634,0.0003017949,0.000128849,0.0000154069,0.0001498345,0.0002035111,0.0005323224],"category_scores_gemma":[0.0001785682,0.0001838307,0.00005845665,0.0005581392,0.0000536778,0.00008771208,0.00003363599,0.0007458127,0.0001457921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002844228,"about_ca_system_score_gemma":0.00008070533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009856212,"about_ca_topic_score_gemma":0.0004344996,"domain_scores_codex":[0.9974657,0.0007648696,0.0004474523,0.0004469933,0.0001609935,0.0007140474],"domain_scores_gemma":[0.9990486,0.0005681622,0.0001278137,0.0001714517,0.00003326997,0.00005069543],"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":[0.0002628885,0.0005348797,0.009242405,0.0000248543,0.00002135974,0.002808508,0.008760832,0.001063739,0.001705893,0.0004242143,0.001512487,0.9736379],"study_design_scores_gemma":[0.05071178,0.007253745,0.4217504,0.02079281,0.0001635716,0.08279154,0.0524391,0.1322072,0.02428971,0.1555382,0.03831944,0.01374251],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9816498,0.0007788801,0.005970023,0.000221563,0.0004635251,0.0002798532,7.654054e-7,0.0001330595,0.01050258],"genre_scores_gemma":[0.9980939,0.00009728856,0.0007043349,0.0004182346,0.0002214097,0.00002267706,0.00004167578,0.00002368582,0.0003768045],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9598954,"threshold_uncertainty_score":0.7496399,"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."}}