{"id":"W2980375914","doi":"10.48550/arxiv.1912.06791","title":"Approximations in Probabilistic Programs","year":2019,"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 Toronto","funders":"","keywords":"Markov chain; Ergodic theory; Computer science; Mathematics; Discrete mathematics; Applied mathematics; Algorithm; Pure mathematics; Statistics","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":[0.0002374003,0.0002327944,0.0002655938,0.0002190342,0.00005380654,0.0001653323,0.001634593,0.0002531362,0.000006988086],"category_scores_gemma":[0.00002844844,0.0002656935,0.0001093722,0.0006195442,0.00006869777,0.000278865,0.001193502,0.0006121976,0.0001553462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001785076,"about_ca_system_score_gemma":0.0003163167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001347615,"about_ca_topic_score_gemma":0.00006417088,"domain_scores_codex":[0.9983087,0.0001066874,0.0001985529,0.0009796335,0.00007865331,0.0003277306],"domain_scores_gemma":[0.998421,0.00005081421,0.0001402606,0.001171158,0.0001173785,0.00009938398],"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":[0.000004354835,0.0001532214,0.002146013,0.0001207214,0.00001389668,0.00005037689,0.0002448065,0.4539631,0.000002942553,0.5410812,0.00003358133,0.002185764],"study_design_scores_gemma":[0.0001634074,0.00002744202,0.0004374891,0.0001354398,0.00001062917,0.000001712251,0.00001872613,0.8035571,0.000003885302,0.1953399,0.00004828705,0.0002560093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1014564,0.00002764491,0.8933789,0.0001009897,0.0003074382,0.0005018326,0.000003281364,0.000242552,0.003980915],"genre_scores_gemma":[0.9924616,0.00002575177,0.006541419,0.00003816362,0.00002136475,0.000004348856,0.00001694474,0.00001076524,0.0008796622],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8910052,"threshold_uncertainty_score":0.9999796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1152313063274419,"score_gpt":0.1977702044208664,"score_spread":0.08253889809342452,"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."}}