{"id":"W4400418791","doi":"10.3390/s24134398","title":"Predicting the Arousal and Valence Values of Emotional States Using Learned, Predesigned, and Deep Visual Features","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Outaouais","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Valence (chemistry); Arousal; Computer science; Convolutional neural network; Artificial intelligence; Deep learning; Feature extraction; Cognition; Feature (linguistics); Pattern recognition (psychology); Machine learning; Psychology","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":[0.0002312162,0.00008829998,0.0000876887,0.00006889039,0.000109341,0.00004574872,0.00003143665,0.00006855286,0.00008881402],"category_scores_gemma":[0.00004499199,0.00006380911,0.00002834968,0.00008498695,0.0001743679,0.00005678703,0.0000233988,0.0001421043,0.000006206912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007680807,"about_ca_system_score_gemma":0.00001161197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007461167,"about_ca_topic_score_gemma":0.00001077564,"domain_scores_codex":[0.9992384,0.0001634953,0.0001355511,0.0002113952,0.0001197474,0.0001313719],"domain_scores_gemma":[0.9995602,0.0002622053,0.0000416991,0.00006278662,0.0000373979,0.00003572398],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001227278,0.0007553104,0.1330801,0.002669799,0.002325219,0.000305177,0.2145002,0.008187956,0.05943613,0.0289026,0.005007935,0.5436023],"study_design_scores_gemma":[0.002561112,0.001602671,0.5580412,0.002114272,0.0006934265,0.00319793,0.06308527,0.3281219,0.008040966,0.0302427,0.001238807,0.001059738],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995908,0.001952795,0.0003937556,0.0002465892,0.0003921483,0.0001321583,0.00001635981,0.00006249313,0.0008957129],"genre_scores_gemma":[0.9984309,0.0001593265,0.0003140748,0.00004978375,0.0001559692,0.000002232772,0.000009034107,0.00001426308,0.0008643958],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5425426,"threshold_uncertainty_score":0.260206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03111234844892857,"score_gpt":0.3379729311824622,"score_spread":0.3068605827335336,"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."}}