{"id":"W123387184","doi":"10.1007/978-3-642-31951-8_8","title":"Learning Directed Relational Models with Recursive Dependencies","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Statistical relational learning; Theoretical computer science; Relational database; Predicate (mathematical logic); Probabilistic logic; Artificial intelligence; Bayesian network; Inductive logic programming; Machine learning; Algorithm; Data mining; Programming language","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"],"consensus_categories":[],"category_scores_codex":[0.0007760503,0.0005388571,0.0004621728,0.0006047598,0.0004017131,0.000399678,0.001989843,0.0003653318,0.00002333333],"category_scores_gemma":[0.00006930435,0.0004537574,0.00008830392,0.0005853007,0.0005645208,0.001422248,0.0007410263,0.001477803,0.00005099949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002712147,"about_ca_system_score_gemma":0.0007503018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002834024,"about_ca_topic_score_gemma":0.00005000181,"domain_scores_codex":[0.9961579,0.00005873353,0.0004144171,0.00132434,0.001271136,0.0007735019],"domain_scores_gemma":[0.9976278,0.0004620697,0.0003146324,0.000855081,0.0004982091,0.0002421843],"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":[0.00001042937,0.00002223004,0.00008706208,0.00001739523,0.00001769102,0.00004094388,0.001792413,0.431864,0.00002732731,0.2612487,0.00001007756,0.3048617],"study_design_scores_gemma":[0.0001380664,0.0001597365,0.00006587731,0.0003399284,0.00000993862,0.0001027204,2.800736e-7,0.6655767,0.0001630741,0.3326545,0.0002263456,0.0005628509],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00008608901,0.0008718021,0.9904904,0.0004171288,0.0005471855,0.0002190583,0.000002455125,0.000398875,0.006966957],"genre_scores_gemma":[0.3121804,0.00008234187,0.6863593,0.0003518764,0.0002860978,0.00001008565,0.000008574475,0.0000369936,0.0006843066],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3120943,"threshold_uncertainty_score":0.9997914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03059491554346118,"score_gpt":0.2327220937547221,"score_spread":0.2021271782112609,"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."}}