{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001834627,0.0001677506,0.000217806,0.00005668636,0.0001631158,0.0002365157,0.00004095504,0.00005799687,0.00001775195],"category_scores_gemma":[0.00005023135,0.0001053347,0.00001152111,0.00005286274,0.0000385199,0.0003022797,0.00002035683,0.00003933467,0.00000713795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002838926,"about_ca_system_score_gemma":0.000009750591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007010297,"about_ca_topic_score_gemma":0.00002031513,"domain_scores_codex":[0.9991964,0.00002461231,0.0002619851,0.0001948823,0.00008134082,0.0002407675],"domain_scores_gemma":[0.9996747,0.00003764883,0.00002774352,0.0001214896,0.00006139726,0.00007700923],"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":[0.0002154011,0.0001016817,0.1260932,0.001290257,0.0004820278,0.0001090866,0.005707683,0.07498563,0.7040144,0.002856694,0.02919381,0.05495007],"study_design_scores_gemma":[0.01263849,0.00145699,0.09106867,0.01321433,0.0005299766,0.0006622142,0.004001643,0.04856715,0.7392701,0.0002137105,0.08320083,0.005175926],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9520254,0.0006816097,0.0452734,0.0002171898,0.001207866,0.0001837597,0.00003592026,0.0002002831,0.0001745388],"genre_scores_gemma":[0.9895136,0.002699674,0.00565345,0.00001185302,0.0004251827,0.00005137004,0.000007426419,0.0000359269,0.00160157],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05400702,"threshold_uncertainty_score":0.4295423,"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."}}