{"id":"W1499722775","doi":"10.48550/arxiv.1302.1527","title":"Structured Arc Reversal and Simulation of Dynamic Probabilistic Networks","year":2013,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Exploit; Probabilistic logic; Computer science; Bayesian network; Variable (mathematics); Overhead (engineering); Dynamic Bayesian network; Conditional probability; Sampling (signal processing); Algorithm; Machine learning; Artificial intelligence; Mathematics; Statistics; Computer security","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004236157,0.0007571616,0.0009459159,0.001378628,0.0007473375,0.001699376,0.002805285,0.001482235,0.005111313],"category_scores_gemma":[0.03261456,0.0009437923,0.001318089,0.001486006,0.001916339,0.00341245,0.002646714,0.002948258,0.0008190364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001746176,"about_ca_system_score_gemma":0.002339117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006114508,"about_ca_topic_score_gemma":0.006629917,"domain_scores_codex":[0.9974623,0.001284448,0.0001615663,0.0005019538,0.0004533253,0.0001364403],"domain_scores_gemma":[0.9776334,0.01762477,0.001006449,0.002217212,0.00115206,0.0003661493],"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":[0.0001383411,0.00003537912,0.001721896,0.00006860488,0.00004523522,0.000144415,0.0001683403,0.8062667,0.0009826155,0.1501307,0.000873908,0.03942383],"study_design_scores_gemma":[0.00001480121,0.000009687265,0.00006193364,0.000009147137,0.000006034839,0.000027647,0.000008731215,0.9130306,0.0007531607,0.0852884,0.0007810729,0.00000877627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006284002,0.000037339,0.9922413,0.0001031064,0.0000130306,0.00003928095,0.000116395,0.0003912549,0.0007743719],"genre_scores_gemma":[0.2533625,0.0001282176,0.7435704,0.0001351779,0.00002629054,0.0003404576,0.0007411833,0.0002248167,0.001470874],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006114508,"threshold_uncertainty_score":0.02240324,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03239881429480029,"score_gpt":0.179168524684486,"score_spread":0.1467697103896857,"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."}}