{"id":"W2551541876","doi":"10.1115/ipc2016-64193","title":"Pipeline Diagnostics With Ultrasonic Meters","year":2016,"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":"","funders":"","keywords":"Pipeline (software); Pipeline transport; Ultrasonic sensor; Metering mode; Instrumentation (computer programming); Leak; Computer science; Ultrasonic flow meter; Flow measurement; Real-time computing; Transient (computer programming); Engineering; Reliability engineering; Mechanical engineering; Operating system; Acoustics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006285791,0.0005879872,0.0005016474,0.001921221,0.0004783408,0.001191951,0.0009717765,0.0006416674,0.00359087],"category_scores_gemma":[0.002380412,0.0003556164,0.000238813,0.002825632,0.0003994086,0.001249194,0.0008095233,0.0006589941,0.001751837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001127386,"about_ca_system_score_gemma":0.00119099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01219937,"about_ca_topic_score_gemma":0.02118094,"domain_scores_codex":[0.9981623,0.0001398938,0.00006273179,0.0002157925,0.001319399,0.00009991802],"domain_scores_gemma":[0.9991046,0.0002356985,0.0001377215,0.00009474516,0.000400243,0.00002710386],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007434261,0.0002603501,0.06964616,0.0007780436,0.00007180279,0.0006137926,0.001884633,0.01686409,0.198126,0.004230658,0.02145807,0.6853229],"study_design_scores_gemma":[0.0001250156,0.001557053,0.1108642,0.000458682,0.0002189842,0.002026252,0.002428785,0.1766318,0.4074167,0.004290841,0.2936668,0.0003149243],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4183832,0.003642601,0.4993477,0.001270496,0.0004792656,0.0008254065,0.005963354,0.02259921,0.04748882],"genre_scores_gemma":[0.821815,0.001534407,0.1558937,0.0003786218,0.00007391965,0.0001561169,0.002019279,0.000308365,0.01782057],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01219937,"threshold_uncertainty_score":0.02425671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007244105538934597,"score_gpt":0.1842393184510892,"score_spread":0.1769952129121546,"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."}}