{"id":"W4391054376","doi":"10.2316/j.2024.201-0376","title":"BIMODAL EMOTION DEPTH RECOGNITION METHOD OF FACIAL EXPRESSION AND POSTURE IN CYBER-PHYSICAL SYSTEMS, 1-10.","year":2024,"lang":"en","type":"article","venue":"Mechatronic systems and control","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Facial expression; Face (sociological concept); Facial expression recognition; Emotion recognition; Feature (linguistics); Facial recognition system; Feature extraction; Computer science; Expression (computer science); Artificial intelligence; Psychology; Computer vision; Pattern recognition (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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001820747,0.0003551914,0.0002816498,0.0004345526,0.0001429801,0.0002902596,0.0003372723,0.0002810235,0.006194807],"category_scores_gemma":[0.0003965037,0.0001161716,0.0002319848,0.0002450997,0.0001364691,0.0004119531,0.0003112647,0.0002774905,0.00167362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002101713,"about_ca_system_score_gemma":0.000215842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001195816,"about_ca_topic_score_gemma":0.002487314,"domain_scores_codex":[0.9998204,0.00001928486,0.000006543484,0.00004468437,0.00008423338,0.00002479595],"domain_scores_gemma":[0.9999105,0.00001234978,0.00000730047,0.0000104447,0.00005145458,0.000007992987],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004067128,0.00009080677,0.003144088,0.0003195679,0.00003686103,0.00007367501,0.0001491904,0.001941456,0.3627586,0.001397467,0.007133673,0.6225479],"study_design_scores_gemma":[0.00008838616,0.0007982805,0.1121101,0.0001031436,0.0001967269,0.001619305,0.0004848174,0.3845873,0.4699861,0.00368301,0.02619344,0.0001493312],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1203296,0.002077053,0.8579999,0.0002465983,0.0004169827,0.0002641872,0.001070508,0.001826018,0.01576924],"genre_scores_gemma":[0.7621831,0.001215059,0.2163474,0.000266665,0.0001205682,0.000256059,0.001000794,0.000140465,0.01846995],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006194807,"threshold_uncertainty_score":0.0207237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01380279976856945,"score_gpt":0.2605516198239075,"score_spread":0.246748820055338,"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."}}