{"id":"W2028339849","doi":"10.1115/1.4002496","title":"Optimal Design of Onshore Natural Gas Pipelines","year":2011,"lang":"en","type":"article","venue":"Journal of Pressure Vessel Technology","topic":"Marine and Offshore Engineering Studies","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Optimal design; Pipeline transport; Reliability engineering; Monte Carlo method; Mathematical optimization; Engineering; Constraint (computer-aided design); Computer science; Mathematics; Statistics; Environmental engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001768289,0.0001629057,0.0004002635,0.0004529295,0.00002048875,0.000004363746,0.0003712257,0.0001669513,0.00003593495],"category_scores_gemma":[0.0001266289,0.0001335721,0.00008096285,0.0002723598,0.00008896307,0.000104323,0.00006724181,0.0004494605,0.00000204014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009330769,"about_ca_system_score_gemma":0.00001762083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002093569,"about_ca_topic_score_gemma":7.633863e-7,"domain_scores_codex":[0.9991296,0.00001327947,0.0004376715,0.00008005383,0.0001317568,0.0002076426],"domain_scores_gemma":[0.9993962,0.00004708572,0.0001356423,0.0001855188,0.0002019768,0.00003357442],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006647111,0.0006814951,0.02023011,0.00207851,0.007063183,0.000910238,0.004625188,0.5993972,0.1544941,0.01171926,0.1222998,0.07583617],"study_design_scores_gemma":[0.003550467,0.002529463,0.01259997,0.0008593426,0.001735764,0.002431648,0.001818759,0.1306431,0.7078435,0.007041691,0.1270766,0.001869712],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5220998,0.07043829,0.3974318,0.0006675862,0.004460897,0.0004875101,0.00001451819,0.001102461,0.003297213],"genre_scores_gemma":[0.951824,0.00069593,0.04725465,0.000003924665,0.0001136982,0.000003157215,2.676885e-7,0.00002586269,0.00007851423],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5533494,"threshold_uncertainty_score":0.5446913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01458827442046283,"score_gpt":0.207820914615105,"score_spread":0.1932326401946422,"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."}}