{"id":"W1999823208","doi":"10.5555/777092.777187","title":"Enhancing Davis Putnam with extended binary clause reasoning","year":2002,"lang":"en","type":"article","venue":"","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":87,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"DPLL algorithm; Backtracking; Computer science; Heuristics; Boolean satisfiability problem; Binary number; Solver; Satisfiability; Theoretical computer science; Heuristic; Conjunctive normal form; Algorithm; Artificial intelligence; Mathematics; Programming language; Arithmetic","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":[0.00007882024,0.00008869747,0.00007745907,0.00008787312,0.0001269531,0.0001294161,0.0001770497,0.00003089128,0.0007993817],"category_scores_gemma":[0.00001783738,0.00007208734,0.0000217423,0.0003378069,0.00002418883,0.0005966302,0.00005483671,0.00008027453,0.0001129136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002856875,"about_ca_system_score_gemma":0.00001665848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001595924,"about_ca_topic_score_gemma":0.00004448549,"domain_scores_codex":[0.9992672,0.00002573531,0.0001223024,0.0002527112,0.0001632705,0.0001688007],"domain_scores_gemma":[0.9995077,0.00003065286,0.00004683804,0.0002901022,0.00004864672,0.00007604554],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002022363,0.0002971232,0.007573633,0.00003121696,0.00007957774,0.0002287482,0.004313387,0.007720389,0.007637268,0.1905691,0.006274257,0.7752551],"study_design_scores_gemma":[0.000600145,0.0001646471,0.01447666,0.0000569305,0.000007730551,0.0001726298,0.000136555,0.978354,0.00358137,0.0001433864,0.001958923,0.000346993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007680342,0.00002989808,0.9626411,0.0008251495,0.000112088,0.00008127707,2.122177e-7,0.0003767941,0.02825315],"genre_scores_gemma":[0.7194973,0.00002061704,0.2775578,0.0003534734,0.00002053528,0.000005030794,9.295761e-7,0.000005908192,0.002538463],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9706336,"threshold_uncertainty_score":0.8752669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01142885634625179,"score_gpt":0.2137317807357935,"score_spread":0.2023029243895417,"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."}}