{"id":"W4405360806","doi":"10.1115/ipc2024-130959","title":"Design and Testing of a Flow Facility for Pipeline Leak Prediction, Detection, and Investigation","year":2024,"lang":"en","type":"article","venue":"Volume 3: Operations, Monitoring, and Maintenance; Materials and Joining","topic":"Water Systems and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Pipeline (software); Leak detection; Computer science; Leak; Pipeline transport; Flow (mathematics); Reliability engineering; Petroleum engineering; Engineering; Operating system; Mechanics; Mechanical engineering","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":[0.001132332,0.0006394166,0.0005670013,0.001178403,0.0006142284,0.0004184405,0.001522153,0.0009306175,0.002375314],"category_scores_gemma":[0.00142248,0.0003492075,0.0003586828,0.0003316632,0.0005080074,0.0007139569,0.0005902132,0.0003663927,0.0003802151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006805216,"about_ca_system_score_gemma":0.001519812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001707163,"about_ca_topic_score_gemma":0.00117957,"domain_scores_codex":[0.9992466,0.0001223,0.00005062158,0.0001809529,0.0002668698,0.0001327579],"domain_scores_gemma":[0.9985442,0.0003092721,0.0003144038,0.0002068958,0.0004361016,0.0001890759],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001771099,0.001566223,0.02607274,0.0006725296,0.00006188845,0.0006185652,0.0002220432,0.0706539,0.7896888,0.001648036,0.002847663,0.1041766],"study_design_scores_gemma":[0.0004607857,0.009326213,0.02850352,0.00007614355,0.000109743,0.0005146056,0.0001270053,0.2922647,0.6603127,0.0004440963,0.007706037,0.000154456],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7211723,0.0001359388,0.2693784,0.0001682699,0.00007608224,0.001427067,0.0009083254,0.005567745,0.001165877],"genre_scores_gemma":[0.9396465,0.00003753393,0.0586724,0.00004361967,0.00001110068,0.0004711839,0.0002695344,0.00004898869,0.0007990871],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002375314,"threshold_uncertainty_score":0.007946193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02185974832569496,"score_gpt":0.2078094261902479,"score_spread":0.1859496778645529,"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."}}