{"id":"W2169733173","doi":"10.1109/icpr.1988.28243","title":"PIS: a probabilistic inference system","year":2003,"lang":"en","type":"article","venue":"","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Probabilistic logic; Inference; Categorical variable; Computer science; Set (abstract data type); Fiducial inference; Data mining; Artificial intelligence; Statistical inference; Observable; Machine learning; Mathematics; Statistics; Frequentist inference; Bayesian inference; Bayesian probability","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":[0.0002671444,0.0001082055,0.0001156786,0.00004426876,0.00007794344,0.0001657243,0.0005083653,0.0000469207,0.00001381877],"category_scores_gemma":[0.0001193571,0.00008704913,0.00003226587,0.0002718722,0.00002488418,0.0002296112,0.00005733036,0.00009090295,0.0002848146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004041618,"about_ca_system_score_gemma":0.0001330361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002009499,"about_ca_topic_score_gemma":0.000003415386,"domain_scores_codex":[0.9990177,0.00007536742,0.0001777661,0.0003156218,0.0001715287,0.0002420137],"domain_scores_gemma":[0.9991825,0.00007159133,0.0000365829,0.0005115291,0.00009245199,0.0001053397],"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":[2.761259e-7,0.00001618574,0.00006574566,0.00001975698,0.000002448511,0.000004185187,0.00007093522,0.0002473557,0.00004831121,0.9968902,0.0001456092,0.002489033],"study_design_scores_gemma":[0.0003753671,0.0001557285,0.0002165633,0.0001516604,0.000009766939,0.0001267716,0.00008657561,0.7729127,0.002334235,0.2202794,0.002715124,0.000636132],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002306125,0.00003261134,0.9074168,0.000090674,0.0001810852,0.00008332235,2.612285e-7,0.0004571575,0.08943202],"genre_scores_gemma":[0.9126022,0.000001603798,0.08662686,0.0001423365,0.000007575973,0.00001963218,1.715102e-7,0.000004035736,0.000595584],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9102961,"threshold_uncertainty_score":0.3660811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02616978624568771,"score_gpt":0.2506365703049205,"score_spread":0.2244667840592328,"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."}}