{"id":"W2949645714","doi":"10.48550/arxiv.1303.1483","title":"Using Causal Information and Local Measures to Learn Bayesian Networks","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Bayesian network; Computer science; Minimum description length; Domain (mathematical analysis); Task (project management); Computation; Artificial intelligence; Machine learning; Bayesian probability; Causal structure; Algorithm; Theoretical computer science; Mathematics","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":[0.0002486523,0.0002762216,0.0002586272,0.0002505277,0.0001807432,0.0004115729,0.0008856399,0.0003229355,0.000008947459],"category_scores_gemma":[0.00002119661,0.0003133796,0.00007003886,0.000377595,0.00008819997,0.0009635809,0.001492213,0.0005655666,0.00005304743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001315795,"about_ca_system_score_gemma":0.000181751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009326559,"about_ca_topic_score_gemma":0.00005076733,"domain_scores_codex":[0.9986161,0.0001083778,0.000219227,0.000590405,0.0001030938,0.0003627875],"domain_scores_gemma":[0.9986173,0.00003709134,0.0001533507,0.000654247,0.0002270932,0.0003109132],"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":[0.000008110708,0.00001015922,0.0002892578,0.00002295524,0.00002644611,0.00001586983,0.0002438127,0.9317997,0.000006164142,0.05407476,0.0001474794,0.01335525],"study_design_scores_gemma":[0.0001322349,0.000035068,0.0002425082,0.00009185976,0.00003010455,0.000007474771,0.00005386494,0.9863211,0.00001436466,0.01251002,0.0002018846,0.0003595384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03711348,0.00005492452,0.961336,0.00009306915,0.0003419066,0.00021698,0.000002720758,0.0001786432,0.0006622516],"genre_scores_gemma":[0.9902001,0.0000670594,0.009270746,0.0002794703,0.00005729296,8.282182e-7,0.000005877226,0.000009856771,0.0001087738],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9530866,"threshold_uncertainty_score":0.9999318,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08927615638073368,"score_gpt":0.2039135353837769,"score_spread":0.1146373790030432,"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."}}