{"id":"W1827386195","doi":"10.1007/3-540-45108-0_58","title":"Inspecting and Visualizing Distributed Bayesian Student Models","year":2000,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Bayesian network; Computer science; Visualization; Bayesian probability; Bayesian inference; Artificial intelligence; Variable-order Bayesian network; Process (computing); Graphical model; Machine learning; Data science","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","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0008719184,0.0005050306,0.0005434196,0.0005891494,0.000485727,0.001054484,0.001819801,0.0002386471,0.000009906865],"category_scores_gemma":[0.00003216378,0.0004921731,0.00009918331,0.0004062477,0.0002680723,0.0007740401,0.001065868,0.0008857141,0.00001505484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003421165,"about_ca_system_score_gemma":0.0002028816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006877785,"about_ca_topic_score_gemma":0.00002338295,"domain_scores_codex":[0.9962164,0.00005000816,0.0005746336,0.001543726,0.0009241341,0.0006911268],"domain_scores_gemma":[0.9983056,0.00029376,0.0002722103,0.0007922061,0.0001510458,0.0001852149],"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":[0.000003532797,0.00002487272,0.0001395525,0.00003910337,0.0000211616,0.0001560263,0.003809059,0.188606,0.00005653696,0.3314616,0.000002332753,0.4756803],"study_design_scores_gemma":[0.0001948577,0.0001573847,0.0001634038,0.001082755,0.000008004938,0.0001196217,0.000002210793,0.8633316,0.0002879759,0.1309244,0.002885151,0.0008426545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0003519179,0.0006877884,0.9934662,0.0001546615,0.0009080037,0.0003000582,0.000003500486,0.0002232459,0.003904641],"genre_scores_gemma":[0.8743635,0.0001163711,0.1235189,0.0004414043,0.0007508281,0.000009240371,0.000005440257,0.00005796537,0.0007363578],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8740116,"threshold_uncertainty_score":0.9999825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02302677697127483,"score_gpt":0.2714861585448253,"score_spread":0.2484593815735505,"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."}}