{"id":"W2086056248","doi":"10.1109/fmcad.2007.13","title":"Boosting Verification by Automatic Tuning of Decision Procedures","year":2007,"lang":"en","type":"article","venue":"","topic":"Formal Methods in Verification","field":"Computer Science","cited_by":94,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Heuristics; Parameterized complexity; Solver; Model checking; Boosting (machine learning); Software; Machine learning; Satisfiability modulo theories; Heuristic; Artificial intelligence; Bounded function; Process (computing); Algorithm; Programming language; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00758256,0.001688191,0.001415598,0.001452838,0.0008031303,0.002295624,0.002492335,0.001457833,0.004024813],"category_scores_gemma":[0.04110152,0.001244307,0.001209163,0.0009921055,0.001896623,0.003462726,0.002679172,0.002988952,0.001707432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001478906,"about_ca_system_score_gemma":0.002600675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001204973,"about_ca_topic_score_gemma":0.002098451,"domain_scores_codex":[0.9887085,0.006160284,0.0007832676,0.001700659,0.001985948,0.0006612427],"domain_scores_gemma":[0.9616891,0.02744726,0.001618766,0.007313064,0.001685075,0.0002466739],"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":[0.0008811416,0.0005928265,0.004136806,0.0006075196,0.0001564865,0.0002090046,0.0004198633,0.4537548,0.05005272,0.04730071,0.00370671,0.4381814],"study_design_scores_gemma":[0.0001638075,0.0001352057,0.0004025344,0.00006856963,0.00006270445,0.000101022,0.00004803331,0.9278995,0.02408765,0.04276799,0.004208278,0.00005464419],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06273591,0.0005731839,0.9232054,0.0003459028,0.00008289206,0.0002850365,0.000109919,0.006736074,0.00592566],"genre_scores_gemma":[0.5308714,0.0002777613,0.4656935,0.0003153843,0.00005868263,0.0003170033,0.0002772977,0.001117722,0.00107123],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00758256,"threshold_uncertainty_score":0.04010087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01916878961442004,"score_gpt":0.3105799987214886,"score_spread":0.2914112091070686,"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."}}