{"id":"W4404134085","doi":"10.1145/3649329.3658489","title":"Engineering an Efficient Preprocessor for Model Counting","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Research Foundation Singapore","keywords":"Computer science; Preprocessor; 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":[0.0001947239,0.00006704376,0.00006308671,0.00004109479,0.00004976463,0.0001109326,0.0001536838,0.00001580222,0.000001895717],"category_scores_gemma":[0.00002355785,0.00005393162,0.00002418314,0.0001163724,0.000004648173,0.0005483096,0.00006976634,0.00003245216,0.000009954957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001506118,"about_ca_system_score_gemma":0.00003572987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004666701,"about_ca_topic_score_gemma":0.000001981598,"domain_scores_codex":[0.999356,0.000002007316,0.0001053784,0.0002936247,0.00009119848,0.0001517982],"domain_scores_gemma":[0.9996221,0.0000390383,0.0000107443,0.0002528977,0.00003710167,0.00003811427],"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":[4.82598e-7,0.000005028915,5.575218e-7,0.0001027065,0.000002397306,0.000001021862,0.0003161308,0.4139863,0.0009840993,0.5822943,0.00008814174,0.002218765],"study_design_scores_gemma":[0.00003184657,0.00001012536,0.0000010553,0.0000370185,0.000001223675,0.000004373146,0.00001817007,0.9847538,0.001952216,0.0002769612,0.01283001,0.00008317076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004078023,0.0001519506,0.994424,0.00006579384,0.0002377186,0.0001298555,0.00001007579,0.0005647779,0.0003378396],"genre_scores_gemma":[0.2972176,9.743198e-7,0.7019975,0.00003781132,0.00006940072,0.00005579083,0.000003901149,0.000009857617,0.0006072535],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5820174,"threshold_uncertainty_score":0.2199268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01758388576889792,"score_gpt":0.2694032089683168,"score_spread":0.2518193231994189,"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."}}