{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000425173,0.000109606,0.0001241969,0.00001866627,0.0006400063,0.0005011932,0.002505947,0.0000619107,0.00001641021],"category_scores_gemma":[0.0001463272,0.00009314039,0.00004151809,0.00003116485,0.0000916937,0.0006931366,0.0008363776,0.00008320015,0.00003505416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001111203,"about_ca_system_score_gemma":0.000152045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005889168,"about_ca_topic_score_gemma":0.0001631244,"domain_scores_codex":[0.9988735,0.00001975393,0.0001987189,0.000471659,0.0001643686,0.0002720313],"domain_scores_gemma":[0.997578,0.00009305826,0.0001151268,0.001985176,0.0001211053,0.0001075757],"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":[0.000009145045,0.00005403635,0.003639321,0.00001041481,0.00003228725,0.000003804185,0.00003345088,0.003130512,0.00001435019,0.9194597,0.01998295,0.05363002],"study_design_scores_gemma":[0.0003362469,0.00002781169,0.005353884,0.00002980749,0.000006031227,0.000004421364,0.000001667923,0.9783697,0.00001555757,0.01337147,0.002341477,0.0001419419],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00005301393,0.00007834152,0.9948844,0.002126368,0.0004451086,0.000158891,0.00001375957,0.0001043473,0.002135784],"genre_scores_gemma":[0.35729,0.000005684576,0.641273,0.000483401,0.000137129,0.00001025905,0.00001977489,0.000005307171,0.0007753829],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9752392,"threshold_uncertainty_score":0.4922476,"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."}}