{"id":"W2135674904","doi":"10.1109/icpp.2010.62","title":"A Machine Learning Approach for Optimizing Parallel Logic Simulation","year":2010,"lang":"en","type":"article","venue":"","topic":"Simulation Techniques and Applications","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Artificial intelligence; Parallel computing","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":[0.001278317,0.00008824367,0.000134332,0.0001283263,0.0002844244,0.0001813339,0.0003288228,0.00008744697,0.0005293905],"category_scores_gemma":[0.001116528,0.000061723,0.00009743844,0.0003419527,0.00003317884,0.0001736957,0.00005424981,0.0001738227,0.00004320338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006602001,"about_ca_system_score_gemma":0.00001308129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001207483,"about_ca_topic_score_gemma":0.000004255139,"domain_scores_codex":[0.9987735,0.00003120334,0.0003619859,0.0003413724,0.0003446468,0.0001473644],"domain_scores_gemma":[0.998135,0.001069091,0.0001398188,0.0003412431,0.0002527878,0.00006200169],"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":[0.00001065463,0.00003931035,0.001109666,0.000001064682,0.000002295802,3.847212e-8,0.00005588862,0.8839535,0.00103009,0.08741223,0.0003272177,0.02605812],"study_design_scores_gemma":[0.0001862557,0.00002160877,0.0002045838,3.590216e-7,0.000003004936,7.607856e-7,0.00005494753,0.9034047,0.0001517538,0.04947849,0.04640985,0.0000837265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00293458,0.000008548075,0.9700193,0.0003713663,0.00003617906,0.0004571785,0.000003622323,0.0002148936,0.02595427],"genre_scores_gemma":[0.5799607,7.790046e-7,0.4164927,0.0001279835,0.00004356966,0.00006652635,0.00001800513,0.000006213731,0.003283566],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5770261,"threshold_uncertainty_score":0.5796455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1782664820640298,"score_gpt":0.4362459695405361,"score_spread":0.2579794874765062,"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."}}