{"id":"W1976654407","doi":"10.1167/10.7.609","title":"What does the emotional face space look like?","year":2010,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Face recognition and analysis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Facial expression; Surprise; Psychology; Anger; Expression (computer science); Cognitive psychology; Happiness; Emotional expression; Sadness; Eyebrow; Face (sociological concept); Communication; Computer science; Social psychology","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":[0.001419822,0.0005522359,0.0003895545,0.001370442,0.0009444513,0.00505702,0.0005119313,0.001267241,0.004846552],"category_scores_gemma":[0.009246488,0.0001800035,0.000478037,0.0009024183,0.00240028,0.004301311,0.001125201,0.0008296833,0.001592266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007421729,"about_ca_system_score_gemma":0.000264955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001101938,"about_ca_topic_score_gemma":0.0008871491,"domain_scores_codex":[0.9988003,0.0006936369,0.00003342665,0.0001862255,0.0001647183,0.0001217749],"domain_scores_gemma":[0.9985883,0.0005597315,0.0002257257,0.0001478198,0.0003231786,0.0001552131],"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":[0.00122474,0.0001038491,0.058511,0.001576106,0.000376775,0.002103538,0.0659799,0.001479087,0.03602409,0.06950612,0.06252848,0.7005864],"study_design_scores_gemma":[0.00007944091,0.0009236886,0.2653468,0.002186897,0.0004066407,0.01389603,0.1549865,0.007889817,0.008441994,0.2049267,0.3403144,0.0006011605],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5834005,0.0475975,0.0733026,0.06787246,0.006314238,0.0002560817,0.002358156,0.001031845,0.2178666],"genre_scores_gemma":[0.9756162,0.005091877,0.007537622,0.004401558,0.001082846,0.0001250008,0.0003256662,0.0001676089,0.005651448],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00505702,"threshold_uncertainty_score":0.0162133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008440018259021916,"score_gpt":0.2662831706865616,"score_spread":0.2578431524275397,"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."}}