{"id":"W2185085875","doi":"10.1016/j.artint.2015.11.002","title":"SATenstein: Automatically building local search SAT solvers from components","year":2015,"lang":"en","type":"article","venue":"Artificial Intelligence","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":71,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Benchmark (surveying); Solver; Parameterized complexity; Boolean satisfiability problem; Local search (optimization); Satisfiability; Task (project management); Theoretical computer science; State (computer science); Algorithm; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003682791,0.000141126,0.0001565335,0.0001108219,0.0001322193,0.0002611647,0.0005532837,0.00007746703,0.0001119783],"category_scores_gemma":[0.0001077332,0.0001422252,0.00005569291,0.00039275,0.0001789644,0.0004864654,0.0002150413,0.0001684811,0.001017963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001233394,"about_ca_system_score_gemma":0.0001379134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004778503,"about_ca_topic_score_gemma":0.00008531598,"domain_scores_codex":[0.9983195,0.0001030296,0.0003829749,0.0003975425,0.0004855087,0.0003114316],"domain_scores_gemma":[0.9989125,0.0001653205,0.00006208868,0.0003811704,0.0002020717,0.0002768374],"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":[0.00002693945,0.00009209018,0.001146676,0.000004569478,0.00002652698,0.00003827766,0.002428401,0.07228913,0.004901001,0.1915883,0.0002235618,0.7272345],"study_design_scores_gemma":[0.00003987183,0.00004585366,0.0007377854,0.00002123285,0.000004508575,0.000007304021,0.0004681119,0.947593,0.02320391,0.02747157,0.0002246278,0.0001821629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07649842,0.00001856364,0.9204543,0.001157324,0.000697527,0.0001253476,0.000003305362,0.00026344,0.0007818172],"genre_scores_gemma":[0.8804225,0.000003928373,0.1191966,0.000277973,0.00006131485,0.000003448703,0.000008256258,0.000007663519,0.00001831983],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8753039,"threshold_uncertainty_score":0.9997599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1141609460867904,"score_gpt":0.3091856572084594,"score_spread":0.195024711121669,"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."}}