{"id":"W2899622522","doi":"10.1115/ipc2018-78791","title":"Optimization of Averaging Window Length and Alarming Hold Time in Volume Balance RTTM Leak Detection to Minimize False Alarms and Spill Volume","year":2018,"lang":"en","type":"article","venue":"Volume 3: Operations, Monitoring, and Maintenance; Materials and Joining","topic":"Water Systems and Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"TransCanada (Canada)","funders":"","keywords":"False positive paradox; Leak; Computer science; False alarm; Sensitivity (control systems); Pipeline (software); Thresholding; Volume (thermodynamics); Transient (computer programming); Reliability (semiconductor); Real-time computing; Set (abstract data type); Data mining; Reliability engineering; Engineering; Artificial intelligence; Power (physics)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004810282,0.0002722158,0.000454603,0.0002263653,0.0002535094,0.0003611414,0.00005904658,0.0001353371,0.00002427754],"category_scores_gemma":[0.00007546561,0.0002729841,0.00001810581,0.0001617705,0.00008079833,0.0005775465,0.0000837796,0.00009140011,0.000004563914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004804541,"about_ca_system_score_gemma":0.00001292157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003862959,"about_ca_topic_score_gemma":0.0001143018,"domain_scores_codex":[0.9984782,0.00006135643,0.0005828257,0.0004060928,0.0001275338,0.000343942],"domain_scores_gemma":[0.9994745,0.00001617312,0.00009145439,0.0001613689,0.0001344403,0.0001220882],"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":[0.0002003817,0.00005460828,0.1544202,0.0009211649,0.0001274139,0.00001731759,0.0119905,0.3482092,0.4751031,0.00007769212,0.0005635308,0.008314944],"study_design_scores_gemma":[0.002011094,0.0003353114,0.08132102,0.001231008,0.00006036659,0.00007944557,0.0009761832,0.8689598,0.04226825,0.0000170876,0.001901787,0.0008386957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9540352,0.0005184328,0.04389514,0.00007827221,0.0008700458,0.0004085874,0.00002803509,0.00009387346,0.00007243421],"genre_scores_gemma":[0.9866694,0.0007755833,0.01127818,0.00002164429,0.000466539,0.00005519207,0.00001470694,0.00005029319,0.0006685124],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5207506,"threshold_uncertainty_score":0.9999722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00608322463088912,"score_gpt":0.1922342254361406,"score_spread":0.1861510008052515,"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."}}