{"id":"W2053282624","doi":"10.1115/ipc2004-0378","title":"Multi-Objective Optimization of Large Pipeline Networks Using Genetic Algorithm","year":2004,"lang":"en","type":"article","venue":"2004 International Pipeline Conference, Volumes 1, 2, and 3","topic":"Refrigeration and Air Conditioning Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"TransCanada (Canada); Nova Chemicals (Canada)","funders":"","keywords":"Crossover; Mathematical optimization; Pipeline (software); Gas compressor; Genetic algorithm; Throughput; Selection (genetic algorithm); Computer science; Multi-objective optimization; Population; Pareto principle; Compressor station; Optimization problem; Engineering; Mathematics","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":[0.001181621,0.0009599952,0.0007822302,0.0008844551,0.0004077093,0.000919332,0.0006389573,0.001028208,0.0009634418],"category_scores_gemma":[0.001914513,0.0005508286,0.00053612,0.0009672449,0.0008261132,0.0007238801,0.0005680285,0.0006551357,0.0001272493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001289063,"about_ca_system_score_gemma":0.001080124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006695153,"about_ca_topic_score_gemma":0.005493381,"domain_scores_codex":[0.9996412,0.0001755213,0.00001246269,0.00004700834,0.0000840029,0.00003970454],"domain_scores_gemma":[0.9993838,0.0004497559,0.00006784174,0.00002180415,0.00005663597,0.00002021645],"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":[0.00000683901,0.000006400676,0.00007361807,0.000007108435,0.000005648254,0.00001108184,0.000006073039,0.9958911,0.0002854387,0.000720556,0.00004213792,0.002943873],"study_design_scores_gemma":[0.000004300231,0.00001009848,0.00003319435,0.000001726819,0.000002049682,0.000002548065,0.000003050854,0.9988928,0.0001577032,0.0007853786,0.0001056822,0.000001313338],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1197468,0.0005728317,0.873987,0.0002751891,0.0000291966,0.0001133606,0.00006276532,0.0003651513,0.004847765],"genre_scores_gemma":[0.7645001,0.0004438889,0.2321972,0.00006167949,0.00001867425,0.0002408458,0.0001149549,0.00006557187,0.002357067],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006695153,"threshold_uncertainty_score":0.01331234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01294333656158607,"score_gpt":0.2467132241747337,"score_spread":0.2337698876131477,"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."}}