{"id":"W1988626078","doi":"10.1167/7.9.944","title":"A dynamic facial expression database","year":2010,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Expression (computer science); Facial expression; Computer science; Database; Artificial intelligence; Programming language","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":[0.0003737832,0.00007285044,0.0001126151,0.0001513664,0.00008715937,0.00009778328,0.0004881579,0.00006421813,0.00008571459],"category_scores_gemma":[0.00009183104,0.00005028791,0.00007697358,0.0001204444,0.0000170005,0.001218536,0.0001526293,0.0003633976,0.00006489598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009403833,"about_ca_system_score_gemma":0.00004777013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001403151,"about_ca_topic_score_gemma":0.00000245926,"domain_scores_codex":[0.9990938,0.00003675681,0.000268759,0.0001175267,0.000365213,0.0001179502],"domain_scores_gemma":[0.9992062,0.00004765559,0.0002436922,0.0002469497,0.0001385556,0.0001169241],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002023712,0.00006923127,0.00003803856,0.000003956959,0.000001642658,0.00002984282,0.00009885847,0.000005943685,0.9183916,0.00006215674,0.005247399,0.07603108],"study_design_scores_gemma":[0.006073087,0.002053775,0.03755803,0.001811758,0.00004204911,0.001763711,0.0002371315,0.1245051,0.6359929,0.01511297,0.1738098,0.001039635],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8159263,0.00005506297,0.1806354,0.0008745437,0.002076738,0.00004939541,0.000003025024,0.00002616241,0.0003533631],"genre_scores_gemma":[0.8976563,0.00004265179,0.1019837,0.0001387857,0.0001081078,5.832341e-7,0.000002080579,0.000004757396,0.00006306935],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2823987,"threshold_uncertainty_score":0.2050682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01005485848272777,"score_gpt":0.2917919569237017,"score_spread":0.2817370984409739,"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."}}