{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004159464,0.0007473279,0.0004207759,0.0006088725,0.0002256953,0.0006804714,0.0008266332,0.0008056448,0.0008176632],"category_scores_gemma":[0.001570148,0.0005215118,0.0007078278,0.0004383572,0.0003692153,0.0008588802,0.0005108935,0.0008033813,0.0001847018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00144795,"about_ca_system_score_gemma":0.0008948352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02187033,"about_ca_topic_score_gemma":0.0159695,"domain_scores_codex":[0.9998357,0.00003134003,0.00001077724,0.00004802976,0.00004367558,0.00003046681],"domain_scores_gemma":[0.9994857,0.0002396119,0.0001244491,0.00003588926,0.00009081605,0.00002352984],"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":[0.00006253648,0.00002923466,0.001099127,0.00001919793,0.00002235974,0.00002679859,0.00001281033,0.9797324,0.002366772,0.0005473567,0.0001728015,0.01590856],"study_design_scores_gemma":[4.10539e-7,0.000003351251,0.00008496067,6.157656e-7,0.000001348629,0.000001648091,6.430708e-7,0.9995009,0.0002918918,0.00009102726,0.00002191607,0.000001270126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1201775,0.0002956285,0.8758662,0.0002234351,0.00003501338,0.0000270392,0.000124774,0.001694876,0.001555509],"genre_scores_gemma":[0.9640753,0.0001474989,0.03393762,0.0000341396,0.00001113266,0.0000374805,0.0001658183,0.00004062894,0.001550305],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02187033,"threshold_uncertainty_score":0.04348606,"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."}}