{"id":"W4250404798","doi":"10.32920/ryerson.14668203","title":"Protected multimodal emotion recognition","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Sadness; Computer science; Feature (linguistics); Disgust; Mel-frequency cepstrum; Pattern recognition (psychology); Feature extraction; Artificial intelligence; Emotion classification; Speech recognition; Anger; 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":[0.0005127853,0.0006465316,0.0005909072,0.0005584778,0.0002254114,0.0009670956,0.0006195362,0.0005836344,0.005981181],"category_scores_gemma":[0.001500292,0.0001352009,0.0007157489,0.0003309314,0.0003051211,0.001191118,0.0009421796,0.0005910126,0.00294615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002781126,"about_ca_system_score_gemma":0.0001606662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003120911,"about_ca_topic_score_gemma":0.0002731895,"domain_scores_codex":[0.9994059,0.0001230864,0.00003159668,0.0001797865,0.000187489,0.00007215398],"domain_scores_gemma":[0.9996673,0.00006623237,0.00003393439,0.00007548063,0.0001392543,0.00001773673],"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":[0.0005102718,0.0001442178,0.001670606,0.0003041305,0.00007966031,0.0003677176,0.000358448,0.01122773,0.2002847,0.01133418,0.009319094,0.7643993],"study_design_scores_gemma":[0.00006590549,0.0008538802,0.01670428,0.0002040515,0.0002485386,0.002857892,0.0007470111,0.6171206,0.2561665,0.03152783,0.07331192,0.0001915679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07195519,0.001870837,0.9040507,0.0006620388,0.0005445964,0.0002565458,0.0006406232,0.002435102,0.01758434],"genre_scores_gemma":[0.636061,0.002240342,0.336183,0.0007387331,0.0004756814,0.0003683955,0.001652401,0.0002636744,0.02201679],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005981181,"threshold_uncertainty_score":0.02000904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03400675894368906,"score_gpt":0.2550391256393022,"score_spread":0.2210323666956132,"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."}}