{"id":"W2148730497","doi":"10.1016/j.ijar.2007.08.010","title":"Dynamic multiagent probabilistic inference","year":2007,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University; University of Guelph; Dalhousie University","funders":"","keywords":"Inference; Observable; Computer science; Dynamic Bayesian network; Probabilistic logic; Variable elimination; Domain (mathematical analysis); Bayesian network; State variable; Bayesian probability; Bayesian inference; Artificial intelligence; State (computer science); Mathematical optimization; Algorithm; Mathematics","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":[0.001253704,0.0001318978,0.0001711811,0.0002348262,0.00005277808,0.0002359531,0.001400469,0.00005688241,0.00001151872],"category_scores_gemma":[0.0004458162,0.0001126196,0.0001019869,0.0001680708,0.00004299483,0.0005902057,0.000184262,0.0002857573,0.00001266686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001719009,"about_ca_system_score_gemma":0.000145936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000988057,"about_ca_topic_score_gemma":0.00000450814,"domain_scores_codex":[0.9982796,0.0000326882,0.0005720036,0.0001881186,0.0006756543,0.0002519776],"domain_scores_gemma":[0.998227,0.0001857947,0.0004574622,0.0001881034,0.0007995691,0.0001420854],"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":[0.0001725478,0.0005188709,0.002726974,0.00004346458,0.0003272308,0.001119546,0.003131822,0.01284596,0.009109234,0.4127771,0.0001203045,0.557107],"study_design_scores_gemma":[0.000771679,0.0001600623,0.004308837,0.0005225941,0.00001644615,0.001059422,0.0001178345,0.9591961,0.002660085,0.0303515,0.0005202538,0.000315207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0889369,0.0001448929,0.9084933,0.0003611206,0.0008905018,0.00004795297,0.000001073439,0.00004507493,0.001079213],"genre_scores_gemma":[0.7713873,0.00004170376,0.2283677,0.00009059819,0.00007452697,9.274511e-7,9.826458e-7,0.000006624573,0.00002954275],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9463501,"threshold_uncertainty_score":0.4592495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01754492497511823,"score_gpt":0.3115261802540509,"score_spread":0.2939812552789327,"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."}}