{"id":"W4323363936","doi":"10.1029/2021wr031745","title":"On‐Line Warning System for Pipe Burst Using Bayesian Dynamic Linear Models","year":2023,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Water Systems and Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; McGill University; McGill University Health Centre","funders":"Mitacs","keywords":"Outlier; Constant false alarm rate; Computer science; Data mining; Warning system; Anomaly detection; ALARM; Bayesian probability; Nonlinear system; Set (abstract data type); Data set; Time series; Flow (mathematics); Engineering; Algorithm; Artificial intelligence; Machine learning; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001439588,0.0008414985,0.0009806317,0.001069768,0.0003588008,0.0009935764,0.001329449,0.0007527195,0.002528967],"category_scores_gemma":[0.004152943,0.0005523486,0.0005449784,0.0003829485,0.0002695925,0.0009760528,0.0010392,0.001224424,0.000488936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001075302,"about_ca_system_score_gemma":0.001055181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02099973,"about_ca_topic_score_gemma":0.01553765,"domain_scores_codex":[0.9993279,0.0002565488,0.00003700116,0.0001297733,0.0001741998,0.00007463606],"domain_scores_gemma":[0.9982221,0.0009760354,0.0002817659,0.00007930017,0.0003767228,0.00006412327],"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":[0.0002506102,0.0001582422,0.003197869,0.00006193811,0.00006587213,0.00006209934,0.00006841496,0.9195255,0.002069185,0.002000634,0.001425089,0.07111458],"study_design_scores_gemma":[0.000003251298,0.00001046193,0.0001107961,0.000002047395,0.000002479804,0.000001897736,0.000002344519,0.9993893,0.0001320671,0.0002769158,0.00006623821,0.000002277648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06672949,0.0001669074,0.9262641,0.0003174848,0.00004339888,0.00009458028,0.0002338521,0.003747531,0.002402706],"genre_scores_gemma":[0.947826,0.00007996015,0.04964811,0.00009034183,0.0000235151,0.0001063586,0.0002596407,0.00009562442,0.001870484],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02099973,"threshold_uncertainty_score":0.04175502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0698516885584046,"score_gpt":0.3172933959299705,"score_spread":0.2474417073715659,"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."}}