{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009437267,0.0006510857,0.0006239238,0.0007411177,0.0004215154,0.0008735137,0.0004442522,0.0007613277,0.001792165],"category_scores_gemma":[0.001972388,0.0005506003,0.0005146887,0.0003765126,0.0005888203,0.0005018607,0.0005154074,0.0003555523,0.0001513981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001270247,"about_ca_system_score_gemma":0.002630121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009016925,"about_ca_topic_score_gemma":0.01030428,"domain_scores_codex":[0.9994411,0.0002074693,0.00001810208,0.00006913503,0.0001464991,0.0001176203],"domain_scores_gemma":[0.9995357,0.0002228536,0.00007862521,0.00001761172,0.0001123129,0.00003285379],"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":[0.00003643071,0.00001706435,0.0002396474,0.00003246543,0.000006895046,0.00003356391,0.00002301367,0.9904011,0.001232163,0.003006702,0.00009257154,0.004878473],"study_design_scores_gemma":[0.00002906829,0.0001491059,0.0004429723,0.00001214615,0.00001316901,0.00001691862,0.00005098856,0.993468,0.001172736,0.003898978,0.0007358768,0.00001008698],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4718564,0.0004367912,0.5012349,0.0003191131,0.00002975047,0.0003146467,0.0002064509,0.0001531986,0.02544879],"genre_scores_gemma":[0.9434596,0.0001399735,0.05366009,0.00001947308,0.000003550145,0.0001250032,0.00005861879,0.00001470282,0.002518899],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009016925,"threshold_uncertainty_score":0.0179289,"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."}}