{"id":"W2899904669","doi":"10.1115/ipc2018-78426","title":"Pipeline Rupture Detection Using Real-Time Transient Modelling and Convolutional Neural Networks","year":2018,"lang":"en","type":"article","venue":"Volume 3: Operations, Monitoring, and Maintenance; Materials and Joining","topic":"Water Systems and Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"TransCanada (Canada); University of Calgary","funders":"","keywords":"Computer science; Pipeline (software); Leak; Pipeline transport; Transient (computer programming); False alarm; Upgrade; Real-time computing; Convolutional neural network; Constant false alarm rate; Reliability (semiconductor); Reliability engineering; Preprocessor; Engineering; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.0002432912,0.0002004861,0.0002513919,0.00007449473,0.0004405331,0.00032966,0.00003199699,0.0001229988,0.00001518655],"category_scores_gemma":[0.000007022163,0.0001846943,0.00001803098,0.00006904938,0.00007084379,0.0003083828,0.00002544928,0.00007905142,0.000001217123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003556116,"about_ca_system_score_gemma":0.000008195384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003468163,"about_ca_topic_score_gemma":0.00003307766,"domain_scores_codex":[0.99898,0.00003935958,0.0003632002,0.000257265,0.0000887128,0.0002714112],"domain_scores_gemma":[0.9996516,0.000009341132,0.00004263217,0.00009319646,0.0001140099,0.0000892844],"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":[0.00003469383,0.000007787517,0.0006068758,0.00008357846,0.0000266551,0.00000290139,0.001128014,0.9172035,0.07977311,0.00006110051,0.000188015,0.0008837471],"study_design_scores_gemma":[0.0003684355,0.00005777683,0.00088825,0.0001391323,0.00002896036,0.00005572528,0.0001237093,0.9935291,0.003939786,0.000008577849,0.0006372624,0.0002232791],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.787905,0.0004531688,0.2099791,0.0000203807,0.001302073,0.000167071,0.00001535525,0.0001031618,0.00005467413],"genre_scores_gemma":[0.9907889,0.0007155079,0.006419526,0.00001056291,0.001630063,0.00001819825,0.00001720388,0.00003590662,0.0003641272],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2035596,"threshold_uncertainty_score":0.7531614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01257628856398673,"score_gpt":0.2053780276975496,"score_spread":0.1928017391335629,"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."}}