{"id":"W4413134936","doi":"10.1061/9780784486382.057","title":"Enhancing Pipeline Integrity Management: Predictive Analytics and Probabilistic Applied Load Modeling for Water Transmission Networks","year":2025,"lang":"en","type":"article","venue":"","topic":"Water Systems and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hatch (Canada)","funders":"","keywords":"Pipeline (software); Computer science; Integrity management; Probabilistic logic; Analytics; Pipeline transport; Predictive analytics; Transmission (telecommunications); Reliability engineering; Data science; Environmental science; Engineering; Telecommunications; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.0001938702,0.0001200408,0.0001481214,0.00006278311,0.00005996202,0.00004281431,0.00004914346,0.0000816843,0.00000475832],"category_scores_gemma":[0.000002494602,0.00008535296,0.00002612235,0.00007722036,0.000007402023,0.00006012476,0.00002044652,0.00008871398,5.320865e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006522901,"about_ca_system_score_gemma":0.000004556508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009773286,"about_ca_topic_score_gemma":0.00005038943,"domain_scores_codex":[0.9993364,0.000005578758,0.0002458316,0.0001722039,0.00006230606,0.000177629],"domain_scores_gemma":[0.9997941,0.00001466455,0.000007634176,0.00009608341,0.00005416144,0.00003338135],"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":[0.00002409075,0.000007382152,0.000003624232,0.0005058714,0.00003920634,2.14687e-7,0.0001999117,0.9957361,0.0001342221,0.0008312888,0.0002428576,0.002275223],"study_design_scores_gemma":[0.0003906502,0.000008282611,0.000003252731,0.0001216295,0.00007036085,2.29457e-7,0.00009185995,0.995822,0.001809828,0.00120609,0.0003751324,0.0001007222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002034708,0.00010894,0.9874184,0.00003022181,0.0001300499,0.0006266682,0.000001635536,0.0001809073,0.009468541],"genre_scores_gemma":[0.9866184,0.00005402627,0.01179927,0.0000188606,0.00004546648,0.00007195494,0.00002690651,0.00001549326,0.001349616],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9845837,"threshold_uncertainty_score":0.3480592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007918373420038241,"score_gpt":0.2002620379633819,"score_spread":0.1923436645433436,"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."}}