{"id":"W4406794048","doi":"10.1109/access.2025.3534145","title":"SkelETT—Skeleton-to-Emotion Transfer Transformer","year":2025,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Computing and Algorithms","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Computer science; Transformer; Skeleton (computer programming); Computer vision; Electrical engineering; Engineering; Programming language; Voltage","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002790667,0.0001030092,0.0001350376,0.0001210729,0.0004942659,0.0001382798,0.0004857376,0.00008062172,0.00007067053],"category_scores_gemma":[0.00003274827,0.0001020389,0.00007609706,0.0007661061,0.00007970207,0.00032397,0.000008533147,0.00013397,0.00005259656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007134576,"about_ca_system_score_gemma":0.000116504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006938929,"about_ca_topic_score_gemma":0.0003403058,"domain_scores_codex":[0.9989067,0.00007278985,0.0001691693,0.0002541712,0.0002386978,0.0003584163],"domain_scores_gemma":[0.9995874,0.00008236703,0.00001219208,0.0001374033,0.00007924236,0.0001014476],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002554827,0.0001047375,0.001049174,0.00003726354,0.00003596054,0.000006374208,0.006557583,0.00177149,0.001487856,0.02422008,0.006540923,0.958163],"study_design_scores_gemma":[0.001129301,0.00008422943,0.007616791,0.0002439284,0.0000568411,6.658664e-7,0.001497537,0.0006034193,0.0333224,0.02852926,0.9261814,0.0007342752],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2372604,0.00004421691,0.685234,0.006644026,0.00185852,0.0003282283,0.000003933751,0.0002366987,0.06838993],"genre_scores_gemma":[0.99331,0.00004073248,0.0005479087,0.00161568,0.0003969571,0.00001881396,0.000001523376,0.000009006199,0.004059421],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9574288,"threshold_uncertainty_score":0.4161024,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02519800499558435,"score_gpt":0.3925979422680311,"score_spread":0.3673999372724467,"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."}}