{"id":"W4412989204","doi":"10.1145/3747534","title":"Compiling with Generating Functions","year":2025,"lang":"en","type":"article","venue":"Proceedings of the ACM on Programming Languages","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Programming language","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":[],"consensus_categories":[],"category_scores_codex":[0.0002386933,0.0001194498,0.0001291012,0.00008449903,0.0002380074,0.0002126049,0.001739562,0.00004030253,0.000001103548],"category_scores_gemma":[0.0003310459,0.00007439368,0.00005136565,0.0005861913,0.0000639338,0.0001665679,0.0005467834,0.0001877233,0.000002196225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001980905,"about_ca_system_score_gemma":0.00004105052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001870998,"about_ca_topic_score_gemma":0.00000269748,"domain_scores_codex":[0.9991492,0.000006275182,0.0001638555,0.0002709282,0.0002024872,0.0002072307],"domain_scores_gemma":[0.999167,0.00006055122,0.0001176836,0.0004361715,0.0001890704,0.00002951389],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003480037,0.0002383937,0.01765793,0.0002479958,0.0001277273,0.000001965102,0.002059706,0.0007598012,0.03278874,0.3000441,0.002185667,0.6438532],"study_design_scores_gemma":[0.003694358,0.002301599,0.01279964,0.007874518,0.0004232573,0.000141097,0.01271203,0.1796064,0.6958435,0.06385276,0.01809161,0.002659278],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8217148,0.0005364525,0.1602434,0.007097716,0.0002918557,0.0004547035,0.000001683028,0.0006181438,0.009041255],"genre_scores_gemma":[0.8225938,0.000002499228,0.1763788,0.0002804046,0.00002938339,0.00002587935,3.257114e-7,0.000005926583,0.0006830211],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6630547,"threshold_uncertainty_score":0.3232568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01394400490083882,"score_gpt":0.2641709636345549,"score_spread":0.2502269587337161,"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."}}