{"id":"W2615369482","doi":"10.5006/c2011-11306","title":"Reducing Pipeline Failures in a Complex System Using Statistical Methods","year":2011,"lang":"en","type":"article","venue":"","topic":"Water Systems and Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Devon Energy (Canada)","funders":"","keywords":"Pipeline (software); Computer science; Reliability engineering; Pipeline transport; Engineering; Mechanical engineering; Programming language","routes":{"ca_aff":true,"ca_fund":false,"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":[0.01008226,0.001173182,0.001607255,0.003315371,0.0005569038,0.00147971,0.001316035,0.0007277386,0.001752571],"category_scores_gemma":[0.03082446,0.000792359,0.001210999,0.002086093,0.001214107,0.001856042,0.001223159,0.001159377,0.0001693447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001941289,"about_ca_system_score_gemma":0.00412295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01294321,"about_ca_topic_score_gemma":0.008689444,"domain_scores_codex":[0.9956287,0.002706057,0.0002348169,0.0003866908,0.0008608402,0.0001829131],"domain_scores_gemma":[0.9589685,0.03347936,0.002894678,0.001130983,0.00319967,0.0003268459],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00003548829,0.00006194925,0.001855676,0.00004355903,0.00006670599,0.00002365581,0.00002110173,0.980729,0.0001878737,0.003095943,0.0002126363,0.01366639],"study_design_scores_gemma":[0.000004506754,0.00003632442,0.0002538076,0.000003828723,0.000007356923,0.000003174692,0.000008527915,0.9971197,0.00009432537,0.00236418,0.00009979509,0.000004456148],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07005894,0.0002946162,0.9267683,0.0005411601,0.00004027158,0.0002077929,0.0001356392,0.0005671366,0.001386093],"genre_scores_gemma":[0.8366228,0.0002973544,0.1615443,0.0001076441,0.00005383601,0.0002932156,0.00024545,0.00007873882,0.0007565796],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01294321,"threshold_uncertainty_score":0.05332071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08596117191133416,"score_gpt":0.3005509640469603,"score_spread":0.2145897921356261,"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."}}