{"id":"W2137090072","doi":"10.1016/j.ijar.2010.07.004","title":"Multiagent bayesian forecasting of structural time-invariant dynamic systems with graphical models","year":2010,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"Engineering and Physical Sciences Research Council","keywords":"Computer science; Dynamic Bayesian network; Testbed; Bayesian network; Probabilistic logic; Graphical model; Artificial intelligence; Bayesian probability; Data mining; Machine learning","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":[0.0006444672,0.0001698656,0.0002910955,0.0002523483,0.00006651689,0.0002462102,0.001177569,0.00008429221,0.000006885832],"category_scores_gemma":[0.00008184976,0.0001252988,0.0001109333,0.0001692752,0.00007376231,0.0007859533,0.0001350773,0.0004858629,9.356218e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004473463,"about_ca_system_score_gemma":0.0001577764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000041391,"about_ca_topic_score_gemma":0.000005405996,"domain_scores_codex":[0.9981009,0.00005439351,0.0006147372,0.0002167034,0.0007855663,0.0002276349],"domain_scores_gemma":[0.9979149,0.0001048712,0.0007569869,0.000218115,0.0008640253,0.0001411063],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004319203,0.0002729789,0.002411585,0.0001277036,0.001032321,0.0008937683,0.003302232,0.4006426,0.03883088,0.5041961,0.00003523234,0.04782267],"study_design_scores_gemma":[0.0004544734,0.00010239,0.0001674026,0.0003926178,0.00001582799,0.002302637,0.00004265844,0.9891763,0.000633877,0.006568472,0.000003509572,0.0001398646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2646801,0.00006138014,0.7342241,0.0001508417,0.0005274814,0.00005955597,0.000004533908,0.00002316462,0.0002688736],"genre_scores_gemma":[0.792116,0.000007126273,0.2077487,0.00001375753,0.00008884505,0.00000182256,0.000002039594,0.00001154801,0.00001008643],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5885336,"threshold_uncertainty_score":0.5109538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01407247637237414,"score_gpt":0.24922723352569,"score_spread":0.2351547571533159,"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."}}