{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004904505,0.0001379055,0.0001982294,0.00008181264,0.0002090131,0.0003169831,0.0000177309,0.00007661407,0.000002306486],"category_scores_gemma":[0.0001032688,0.000124116,0.000009440827,0.00007780632,0.00005048065,0.00037199,0.00001959514,0.00004739386,2.998609e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001793331,"about_ca_system_score_gemma":0.0000154153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001023991,"about_ca_topic_score_gemma":0.0000212313,"domain_scores_codex":[0.999177,0.00003392058,0.000367995,0.0002243942,0.00005845995,0.0001382068],"domain_scores_gemma":[0.999684,0.00004723683,0.00002996427,0.00006540586,0.0001155933,0.00005782274],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007065816,0.00001553518,0.01396175,0.004441532,0.0001264361,0.000003262597,0.008467578,0.1666859,0.7650212,0.0002222541,0.001290796,0.03969305],"study_design_scores_gemma":[0.0004228445,0.0001369644,0.004425745,0.0005587055,0.00004966596,0.00003824926,0.0002979846,0.9278053,0.06459325,0.0001281424,0.001346281,0.0001968848],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5746806,0.001293755,0.4225974,0.00003284441,0.0008194491,0.0003587634,0.00007691649,0.0001327724,0.000007489421],"genre_scores_gemma":[0.9721294,0.000489008,0.02672696,0.000003812231,0.000296439,0.0001067833,0.00001926442,0.00001874417,0.0002095642],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7611194,"threshold_uncertainty_score":0.5061303,"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."}}