{"id":"W2158424251","doi":"10.1007/978-3-540-30139-4_6","title":"Evaluating a Probabilistic Model of Student Affect","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Affect (linguistics); Probabilistic logic; Bayesian network; Focus (optics); Statistical model; Dynamic Bayesian network; Machine learning; Arousal; Bayesian probability; Artificial intelligence; Empirical research; Emotion detection; Affective computing; Emotion recognition; Psychology; Social psychology; Statistics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001889186,0.0004639104,0.0006279662,0.0005850952,0.0001857182,0.0002761972,0.002734811,0.0002080798,0.00000822387],"category_scores_gemma":[0.0001576294,0.0004080627,0.0001745702,0.0003667909,0.0003395108,0.000328626,0.001171085,0.0007161478,0.00001783942],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006117653,"about_ca_system_score_gemma":0.001077374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000274345,"about_ca_topic_score_gemma":0.00001243581,"domain_scores_codex":[0.9958301,0.00005292706,0.0006883699,0.001321054,0.001550282,0.0005572223],"domain_scores_gemma":[0.9974422,0.0003969576,0.0005084456,0.001125418,0.0004171382,0.0001098272],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001765695,0.000021854,0.00002627339,0.00007992252,0.000008383254,0.00001206303,0.001248929,0.7182118,0.0004380419,0.212489,2.713931e-7,0.06746174],"study_design_scores_gemma":[0.0001783617,0.0003392125,0.00005349419,0.001463021,0.00000893977,0.0000145113,2.258617e-7,0.8483555,0.0009776856,0.1481804,0.00001999734,0.0004086323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00125037,0.000233293,0.9950238,0.00007790562,0.001010595,0.000612669,0.000001881967,0.0001030316,0.001686412],"genre_scores_gemma":[0.6234294,0.000006262012,0.3753688,0.0001143343,0.0002281454,0.00001274747,8.780973e-7,0.00003009556,0.0008093026],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.622179,"threshold_uncertainty_score":0.9998371,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0724588039669964,"score_gpt":0.3314946900857111,"score_spread":0.2590358861187148,"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."}}