{"id":"W3047490124","doi":"10.18280/ria.340304","title":"Performance Evaluation of Machine Learning for Recognizing Human Facial Emotions","year":2020,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Facial expression; Human–computer interaction; Artificial intelligence; Machine learning; Psychology; Cognitive psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006570797,0.00009742929,0.0001436945,0.00007034031,0.0002808296,0.00004364444,0.0003328075,0.00005078665,0.0001322225],"category_scores_gemma":[0.0003035558,0.00009942512,0.00008402637,0.0003463265,0.00002928473,0.0003311288,0.00008187156,0.0001296145,0.0001165288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002070637,"about_ca_system_score_gemma":0.0000366967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007846985,"about_ca_topic_score_gemma":0.000002874461,"domain_scores_codex":[0.9988355,0.00008113953,0.0003588273,0.0003053684,0.0002403515,0.000178792],"domain_scores_gemma":[0.9991644,0.00008039196,0.0001587994,0.0001845071,0.0003428655,0.00006902638],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001641232,0.00008598832,0.0008368842,0.0001214129,0.00001414233,4.08813e-7,0.004313358,0.169102,0.09109342,0.0009925012,0.0002608925,0.7331626],"study_design_scores_gemma":[0.00006742538,0.0001740897,0.00007616035,0.00006597696,0.00001338498,0.000001239797,0.000221286,0.8425149,0.1550736,0.0004660817,0.001226752,0.00009913477],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3920271,0.00007366704,0.6047406,0.0008564372,0.0001690453,0.0004010419,0.000004808454,0.00009108331,0.001636255],"genre_scores_gemma":[0.9938716,0.0000300658,0.005760966,0.00008072405,0.00007311741,0.00004144061,0.0000258209,0.000008136021,0.000108129],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7330635,"threshold_uncertainty_score":0.4054439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1412725530523142,"score_gpt":0.3237512251745021,"score_spread":0.1824786721221879,"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."}}