{"id":"W2312545794","doi":"10.1115/ipc2006-10058","title":"Application of High-Grade Steels to Onshore Natural Gas Pipelines Using Reliability-Based Design Method","year":2006,"lang":"en","type":"article","venue":"Volume 1: Project Management; Design and Construction; Environmental Issues; GIS/Database Development; Innovative Projects and Emerging Issues; Operations and Maintenance; Pipelining in Northern Environments; Standards and Regulations","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"TransCanada (Canada)","funders":"","keywords":"Reliability (semiconductor); Pipeline transport; Constraint (computer-aided design); Pipeline (software); Reliability engineering; Natural gas; High pressure; Materials science; Structural engineering; Computer science; Mechanical engineering; Engineering; Engineering physics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001040775,0.0005139337,0.0005663539,0.0006503854,0.0006774366,0.0001361178,0.0001233912,0.0001430241,0.00002148932],"category_scores_gemma":[0.00003189141,0.0004683784,0.00004275236,0.0008137068,0.0005769884,0.0004722077,0.0001721903,0.0003034431,0.0000010341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002924078,"about_ca_system_score_gemma":0.00005836555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00128408,"about_ca_topic_score_gemma":0.0002842758,"domain_scores_codex":[0.9970387,0.000221911,0.0009877392,0.0008688469,0.0004441059,0.0004386676],"domain_scores_gemma":[0.9992721,0.00007374064,0.0001602674,0.0003249033,0.00008085254,0.00008814587],"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":[0.000336229,0.0004308788,0.06243404,0.0008205038,0.0006168393,0.00002100915,0.007184482,0.7615789,0.02728986,0.00388623,0.0003156571,0.1350854],"study_design_scores_gemma":[0.002603261,0.0001831468,0.03097307,0.0005758242,0.0002858658,0.00005042361,0.01161013,0.9294241,0.008221591,0.0007748556,0.01385782,0.001439897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3970833,0.0009015467,0.6001225,0.0002160734,0.00008149708,0.00138212,0.0001411196,0.00005149865,0.0000202609],"genre_scores_gemma":[0.5425616,0.0009364409,0.4556891,0.00003211342,0.00003686835,0.0001747246,0.0002359549,0.00004016046,0.0002930696],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.1678452,"threshold_uncertainty_score":0.9997768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01254995439152179,"score_gpt":0.2601329318389304,"score_spread":0.2475829774474086,"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."}}