{"id":"W4287755157","doi":"10.48550/arxiv.2006.10833","title":"","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada; Canadian Institute for Advanced Research","keywords":"Causal model; Computer science; Causal structure; Series (stratigraphy); Causal analysis; Graph; Theoretical computer science; Confounding; Machine learning; Artificial intelligence; Data mining; Mathematics; Econometrics; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0003822493,0.0008076736,0.0008224681,0.0003024085,0.0003542099,0.0006311224,0.008084463,0.0006753781,0.000006232698],"category_scores_gemma":[0.00009285294,0.0009916597,0.0005334305,0.001334173,0.0001962624,0.001056088,0.009737858,0.002077889,0.00008910453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002659521,"about_ca_system_score_gemma":0.0006932848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003884557,"about_ca_topic_score_gemma":0.00004530352,"domain_scores_codex":[0.9945476,0.0003749737,0.0004645116,0.003523296,0.0002125605,0.0008770574],"domain_scores_gemma":[0.9951475,0.0001647162,0.0004924181,0.00298041,0.0003660676,0.0008489427],"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":[0.00003638447,0.00009086532,0.0009861196,0.00009805032,0.0001074767,0.001195589,0.0006441786,0.4094064,0.0001477206,0.5861274,0.0001503604,0.001009479],"study_design_scores_gemma":[0.0003831266,0.00008675978,0.0005528147,0.0001016168,0.00005363358,0.000007520273,0.00004529096,0.6560008,0.0002110845,0.3417679,0.00005044938,0.0007389945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1890635,0.00007069704,0.8060421,0.002098884,0.0006367641,0.00030587,0.00001583315,0.001289898,0.0004764092],"genre_scores_gemma":[0.9835473,0.0002184477,0.01465034,0.001202877,0.0001581488,0.000001931193,0.00001892605,0.00003338353,0.000168615],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7944838,"threshold_uncertainty_score":0.9992534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1539157096516495,"score_gpt":0.1936386869331215,"score_spread":0.03972297728147198,"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."}}