{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001880473,0.0008957791,0.001305661,0.0008990005,0.0004121853,0.001938996,0.002593122,0.001639437,0.005514849],"category_scores_gemma":[0.013414,0.001541772,0.001519407,0.00159801,0.001016643,0.006013911,0.002323152,0.003063463,0.001217503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001048553,"about_ca_system_score_gemma":0.0007416239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003067147,"about_ca_topic_score_gemma":0.005731652,"domain_scores_codex":[0.998774,0.0005316949,0.00007097796,0.0003376402,0.000212613,0.00007301236],"domain_scores_gemma":[0.9908968,0.007871966,0.0002701691,0.0006376064,0.0002126106,0.0001108569],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001433603,0.0001535671,0.001828242,0.0003260905,0.0001976747,0.0002093175,0.0003831322,0.4846381,0.001155007,0.3114704,0.006337102,0.1931581],"study_design_scores_gemma":[0.00001457178,0.00001610428,0.000129078,0.00002635347,0.00002825616,0.00004283636,0.00002082065,0.7176014,0.000332209,0.2803036,0.001471618,0.00001317146],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01183317,0.0005671024,0.9847328,0.000332055,0.00002089742,0.00002011165,0.0003038671,0.0005334605,0.001656463],"genre_scores_gemma":[0.4524585,0.002486652,0.5303541,0.0004060195,0.0001882655,0.0002602656,0.003387992,0.0003338879,0.01012424],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005514849,"threshold_uncertainty_score":0.01844907,"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."}}