{"id":"W1605943448","doi":"10.1109/roman.1995.531971","title":"An evaluation of 3-D emotion space","year":2002,"lang":"en","type":"article","venue":"","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"University of Tokyo","keywords":"Space (punctuation); Facial expression; Expression (computer science); Face (sociological concept); Computer science; Emotion recognition; Emotion classification; Artificial intelligence; Computer vision; Linguistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001415652,0.0005793323,0.0002431313,0.0008700788,0.0002126117,0.0009003888,0.0003242398,0.0005050907,0.00493003],"category_scores_gemma":[0.006851824,0.0001260123,0.0004787712,0.0003736075,0.0004411945,0.0008277991,0.0009003039,0.0002337573,0.0005462238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003238055,"about_ca_system_score_gemma":0.0001450278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008087595,"about_ca_topic_score_gemma":0.0004656123,"domain_scores_codex":[0.9986501,0.0006268973,0.00008493524,0.0000976028,0.0004768088,0.00006371957],"domain_scores_gemma":[0.9974372,0.001176494,0.0001120966,0.0002447526,0.0009128288,0.0001165804],"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":[0.005225612,0.0004185638,0.01151391,0.001349442,0.0003093563,0.000577661,0.001929772,0.1320557,0.3039906,0.0110071,0.00531747,0.5263048],"study_design_scores_gemma":[0.000334382,0.004507801,0.05775381,0.0002042359,0.0002514601,0.00208376,0.003070598,0.7155229,0.1810949,0.009797625,0.02506996,0.0003084425],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5512156,0.0009115707,0.4250236,0.0004078902,0.000315074,0.0003052835,0.0009900669,0.001616211,0.01921466],"genre_scores_gemma":[0.9244191,0.0003241779,0.0724384,0.0000871978,0.00002886391,0.0001521631,0.0007319412,0.000138031,0.001680133],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00493003,"threshold_uncertainty_score":0.01649255,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1368259519140659,"score_gpt":0.3868768216898298,"score_spread":0.250050869775764,"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."}}