{"id":"W2086286112","doi":"10.1007/s10589-008-9175-8","title":"Efficient preprocessing for VLSI optimization problems","year":2008,"lang":"en","type":"article","venue":"Computational Optimization and Applications","topic":"Advanced Optimization Algorithms Research","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Preprocessor; Solver; Very-large-scale integration; Computer science; Integer programming; Mathematical optimization; Linear programming; Constraint (computer-aided design); Algorithm; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006110424,0.001327789,0.001043129,0.001098565,0.0007238367,0.001639975,0.001453204,0.0009219917,0.02021044],"category_scores_gemma":[0.004559675,0.0007060307,0.001069397,0.002049182,0.0005920426,0.001778114,0.001369114,0.002148588,0.005730934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006507316,"about_ca_system_score_gemma":0.001722316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001508733,"about_ca_topic_score_gemma":0.003572572,"domain_scores_codex":[0.999223,0.000203821,0.0000569428,0.0001279528,0.0002508694,0.0001374078],"domain_scores_gemma":[0.9981561,0.0009775606,0.0000909289,0.0004346133,0.00030042,0.00004033284],"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":[0.0006440109,0.0002433444,0.0005772772,0.0006759047,0.00007911371,0.0003280536,0.0001151005,0.1571323,0.02206911,0.08932726,0.044783,0.6840255],"study_design_scores_gemma":[0.0001903347,0.0001795042,0.0005716412,0.00008251018,0.00005758326,0.0002255386,0.00009268717,0.7378821,0.02614774,0.2036263,0.03091209,0.00003193383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01284821,0.0006842719,0.9659905,0.0006405317,0.0001952789,0.0001269032,0.0004996714,0.002841816,0.01617276],"genre_scores_gemma":[0.1790745,0.0008457954,0.8014065,0.0005060284,0.0002615449,0.0004100208,0.003178764,0.001430948,0.01288598],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02021044,"threshold_uncertainty_score":0.06761068,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06567202607348327,"score_gpt":0.356399427834244,"score_spread":0.2907274017607607,"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."}}