{"id":"W2017596757","doi":"10.1115/ipc2004-0020","title":"Dynamic Modelling and Real-Time Leak Detection for NGL Pipelines","year":2004,"lang":"en","type":"article","venue":"2004 International Pipeline Conference, Volumes 1, 2, and 3","topic":"Water Systems and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Pipeline transport; Leak; Transient (computer programming); Computer science; Leak detection; Pipeline (software); Volume (thermodynamics); Simulation; Real-time computing; Engineering; Mechanical engineering","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":[0.0002795015,0.0005272758,0.0005310117,0.0003431312,0.0004358229,0.0008865462,0.000624096,0.0005920322,0.001893055],"category_scores_gemma":[0.0008145583,0.0003498664,0.0004158621,0.0002826996,0.0006428111,0.001107187,0.0005540237,0.0004640462,0.0002791737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001122743,"about_ca_system_score_gemma":0.00136896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01911088,"about_ca_topic_score_gemma":0.007942889,"domain_scores_codex":[0.9997781,0.00005287864,0.00001089293,0.00004098446,0.00009227424,0.00002501674],"domain_scores_gemma":[0.9997879,0.00007910457,0.00004161095,0.00002263772,0.00005609712,0.00001269885],"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":[0.00002993326,0.000009661871,0.0003646871,0.00002513552,0.000003583272,0.00002886125,0.00003977034,0.988689,0.004247868,0.001729039,0.0001425547,0.004689885],"study_design_scores_gemma":[0.000004505412,0.00000861211,0.00008377252,0.000001793391,0.00000154372,0.000006832735,0.000006443096,0.9974056,0.001222416,0.0004324012,0.0008219783,0.000004001227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07235875,0.0001380987,0.9193238,0.0001973629,0.00002430679,0.0000646328,0.0001642072,0.001299809,0.00642913],"genre_scores_gemma":[0.9545445,0.000248884,0.03956756,0.00002947546,0.00000870568,0.0001163008,0.0001973124,0.0001287814,0.005158314],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01911088,"threshold_uncertainty_score":0.03799927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009567724557949058,"score_gpt":0.2089233659362761,"score_spread":0.199355641378327,"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."}}