{"id":"W3091263495","doi":"10.1061/(asce)ps.1949-1204.0000511","title":"Prediction of Breaks in Municipal Drinking Water Linear Assets","year":2020,"lang":"en","type":"article","venue":"Journal of Pipeline Systems Engineering and Practice","topic":"Water Systems and Optimization","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; Sumitomo Precision Products (Canada); SNC-Lavalin (Canada)","funders":"","keywords":"Asset management; Pipeline transport; Pipeline (software); Metric (unit); Predictive modelling; Linear regression; Regression analysis; Asset (computer security); Sensitivity (control systems); Computer science; Environmental science; Data mining; Econometrics; Mathematics; Engineering; Machine learning; Environmental engineering; Operations management; Business","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008796024,0.0001230754,0.0003140339,0.0001531166,0.00001274341,0.00003213413,0.00007860434,0.00008858919,0.000002130042],"category_scores_gemma":[0.0002637245,0.00009767474,0.00004061421,0.0001333533,0.000005246975,0.0005028458,0.00001801133,0.0002937826,0.000001484504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002833095,"about_ca_system_score_gemma":0.000008703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004605385,"about_ca_topic_score_gemma":0.000003026097,"domain_scores_codex":[0.9987633,0.00006682266,0.0007571177,0.00007619848,0.0001944313,0.0001420974],"domain_scores_gemma":[0.9994066,0.0001364176,0.0001531269,0.00007352125,0.0001398556,0.00009051836],"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":[0.00003307968,0.00001146253,0.0002610948,0.0004086497,0.0000467176,0.0000294591,0.002044633,0.9838517,0.01292505,0.00002349461,0.000279667,0.00008502267],"study_design_scores_gemma":[0.0005852639,0.00009910038,0.0001766944,0.0003540644,0.00004596788,0.0004483544,0.000356038,0.972201,0.00194957,5.368216e-7,0.02368435,0.00009905832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4961908,0.006537979,0.4907072,0.0008298433,0.003753714,0.0004118442,0.00002028074,0.0001916101,0.001356783],"genre_scores_gemma":[0.9971182,0.0001989216,0.001990416,0.000008944957,0.0006306394,0.000001694634,0.000003587496,0.0000275221,0.00002004791],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5009275,"threshold_uncertainty_score":0.398306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02225635866992046,"score_gpt":0.2218536888650307,"score_spread":0.1995973301951103,"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."}}