{"id":"W2227333382","doi":"10.1609/socs.v1i1.18180","title":"Computing Equivalent Transformations for Combinatorial Optimization by Branch-and-Bound Search","year":2010,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Maximum satisfiability problem; Solver; Branch and bound; Pruning; Bounding overwatch; Mathematical optimization; Transformation (genetics); Computer science; Context (archaeology); Domain (mathematical analysis); Local search (optimization); Boolean satisfiability problem; Mathematics; Algorithm; Artificial intelligence; Boolean function","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":[0.0009977592,0.000169075,0.0001742974,0.0001108914,0.0004315116,0.0004648263,0.001122524,0.0001279557,0.0000254826],"category_scores_gemma":[0.0001654731,0.0001494642,0.0001139458,0.0003173799,0.0001789147,0.0006465625,0.0002693156,0.0003945359,0.000002576702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001084177,"about_ca_system_score_gemma":0.00009344498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003165333,"about_ca_topic_score_gemma":0.000001224628,"domain_scores_codex":[0.9980323,0.00002188554,0.0004123918,0.0003592004,0.0009045812,0.0002695818],"domain_scores_gemma":[0.9983595,0.0002854015,0.0001611301,0.0001435259,0.0009420246,0.0001084271],"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":[0.0001059206,0.0002168171,0.0009662858,0.00004873084,0.0000515295,5.431373e-8,0.0006221806,0.003320683,0.06847598,0.9215153,0.0008324056,0.003844086],"study_design_scores_gemma":[0.005819885,0.0004132144,0.0008187882,0.0001181415,0.00003095119,0.00002431312,0.0001111051,0.7388023,0.2240735,0.02460315,0.004665529,0.0005192105],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6038961,0.00002232154,0.3147675,0.03623919,0.02633111,0.002964537,0.0001182593,0.0003370802,0.01532382],"genre_scores_gemma":[0.995061,0.00002386755,0.004391838,0.0001010881,0.0002901315,0.00002678323,0.00001442752,0.00001683196,0.00007405379],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8969122,"threshold_uncertainty_score":0.6094974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01213545434067036,"score_gpt":0.2629143388659637,"score_spread":0.2507788845252933,"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."}}