{"id":"W3212439178","doi":"10.1109/jsen.2021.3128816","title":"A BiLSTM Based Pipeline Leak Detection and Disturbance Assisted Localization Method","year":2021,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Water Systems and Optimization","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Leak; Multilateration; Pipeline (software); Computer science; Artificial intelligence; Real-time computing; Engineering; Azimuth; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002058797,0.0001080418,0.0001418089,0.00008252695,0.0001131156,0.0001217785,0.00002933261,0.00007710837,0.00002834948],"category_scores_gemma":[0.00004880015,0.00009968825,0.00004256761,0.0002218629,0.00000965857,0.0001337272,0.000003631506,0.0001618149,0.000003173473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006250121,"about_ca_system_score_gemma":0.00001912951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006266405,"about_ca_topic_score_gemma":0.0002011083,"domain_scores_codex":[0.9992433,0.0001099036,0.000253982,0.0001171303,0.0001370534,0.0001385904],"domain_scores_gemma":[0.9995706,0.00003019828,0.0000566386,0.00009223522,0.0001500432,0.0001002868],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008904838,0.00001113863,0.0002547186,0.00004964596,0.00001928664,0.00004387447,0.0001055186,0.9776817,0.01601683,0.00000247461,0.001581237,0.004224704],"study_design_scores_gemma":[0.0004101193,0.00001325525,0.0007436847,0.00005860979,0.00002324301,0.0006518396,0.00003840325,0.9388341,0.05286896,0.00001734114,0.006217578,0.0001228803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01758824,0.0003587292,0.9803932,0.00006859256,0.001026195,0.00004909461,0.000003075395,0.00008734057,0.0004255118],"genre_scores_gemma":[0.9902352,0.00006012679,0.008572087,0.00005210302,0.0003698659,0.000001964447,0.000005332415,0.00002974398,0.000673542],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.972647,"threshold_uncertainty_score":0.4065169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01196352100186815,"score_gpt":0.2250454398837444,"score_spread":0.2130819188818763,"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."}}