{"id":"W2154501085","doi":"10.1109/icalt.2010.174","title":"The Emotional Machine: A Machine Learning Approach to Online Prediction of User's Emotion and Intensity","year":2010,"lang":"en","type":"article","venue":"","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Support vector machine; Human–computer interaction; Machine learning; Intensity (physics)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004197814,0.000103003,0.0001192115,0.00009402187,0.0001903156,0.00002011924,0.00007506108,0.000112709,0.0003973036],"category_scores_gemma":[0.0001292729,0.00007159013,0.00004633154,0.0001329664,0.00007896542,0.00006048936,0.00004932021,0.0004420984,0.00003012644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008252499,"about_ca_system_score_gemma":0.000008740213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001816049,"about_ca_topic_score_gemma":0.0002090902,"domain_scores_codex":[0.9991496,0.0001003453,0.0002351363,0.0002292813,0.0001426683,0.0001429458],"domain_scores_gemma":[0.9994465,0.00006024044,0.00008559013,0.00015952,0.0001616389,0.00008651502],"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":[0.001564543,0.005102912,0.330924,0.0001531559,0.0006201316,0.000004668171,0.009510035,0.0002652931,0.05723083,0.1420931,0.02037997,0.4321514],"study_design_scores_gemma":[0.001062063,0.0003072878,0.9699893,0.00001574934,0.00003621199,0.0001742002,0.00102336,0.01450954,0.0002513007,0.0004597887,0.01203863,0.0001325904],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9546087,0.00002774435,0.01717478,0.001063398,0.0009850595,0.0002911387,0.00006931776,0.0001071867,0.02567266],"genre_scores_gemma":[0.990802,0.00001807041,0.002381289,0.0002309172,0.0001672326,0.000009375546,0.0002907735,0.00001307853,0.00608726],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6390653,"threshold_uncertainty_score":0.4350196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02582498251570571,"score_gpt":0.2791175788940783,"score_spread":0.2532925963783726,"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."}}