{"id":"W2740374084","doi":"10.24963/ijcai.2017/375","title":"Locally Consistent Bayesian Network Scores for Multi-Relational Data","year":2017,"lang":"en","type":"article","venue":"","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Benchmark (surveying); Relational database; Consistency (knowledge bases); Data modeling; Selection (genetic algorithm); Data mining; Bayesian network; Artificial intelligence; Function (biology); Score; Machine learning; Contrast (vision); Table (database); Database","routes":{"ca_aff":true,"ca_fund":true,"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":[0.01692037,0.001030878,0.001497333,0.004912286,0.0006801018,0.002785293,0.003081384,0.00196682,0.002797159],"category_scores_gemma":[0.07310357,0.0005711074,0.001173764,0.003074693,0.002134652,0.005915523,0.003925879,0.003313892,0.0009283057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002372778,"about_ca_system_score_gemma":0.001638828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002699499,"about_ca_topic_score_gemma":0.004511733,"domain_scores_codex":[0.9861603,0.005955148,0.0008057894,0.002569321,0.004155032,0.000354461],"domain_scores_gemma":[0.9570373,0.02631839,0.003454968,0.007859077,0.004458546,0.0008717758],"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":[0.0005030697,0.000305447,0.02379329,0.0003586032,0.0004694526,0.0002437212,0.0003974948,0.4972803,0.004848788,0.1239973,0.008094549,0.3397081],"study_design_scores_gemma":[0.00003772254,0.0001249714,0.002569075,0.00003719317,0.00003689712,0.0001012591,0.00004801862,0.854902,0.001827784,0.1385374,0.001732821,0.00004485964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03299738,0.0003950169,0.9633881,0.0004085872,0.00002928628,0.00008381969,0.0004612909,0.001023994,0.001212528],"genre_scores_gemma":[0.5756074,0.0003535187,0.4176533,0.000400457,0.0001403456,0.0003313178,0.003149956,0.0004880352,0.001875815],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01692037,"threshold_uncertainty_score":0.08948457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2108087910340802,"score_gpt":0.34990067900421,"score_spread":0.1390918879701298,"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."}}