{"id":"W4400532948","doi":"10.1016/j.tust.2024.105958","title":"A hybrid machine learning-based model for predicting failure of water mains under climatic variations: A Hong Kong case study","year":2024,"lang":"en","type":"article","venue":"Tunnelling and Underground Space Technology","topic":"Water Systems and Optimization","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Mains electricity; Engineering; Forensic engineering; Machine learning; Mechanical engineering; Environmental science; Computer science; Electrical engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008853307,0.001048814,0.0009564729,0.0008783708,0.000504587,0.001061558,0.00114908,0.001024199,0.001340732],"category_scores_gemma":[0.0009557256,0.0004851209,0.0007628273,0.0007889099,0.000501537,0.0006493937,0.000432808,0.0004503398,0.0001819591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002584798,"about_ca_system_score_gemma":0.00144626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2586757,"about_ca_topic_score_gemma":0.1578207,"domain_scores_codex":[0.9997603,0.0000665094,0.00001919589,0.00005947542,0.00002742215,0.00006710466],"domain_scores_gemma":[0.9991907,0.0004345049,0.00008408108,0.00004641676,0.0001938833,0.00005035696],"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":[0.00006314423,0.00006279856,0.009338398,0.00001693602,0.00002992817,0.0001392971,0.00002054318,0.9867662,0.0003031964,0.0001508088,0.0001838991,0.002924878],"study_design_scores_gemma":[0.000002596193,0.00001566604,0.001587259,0.000001313328,0.000005748315,0.000005251991,0.00001643808,0.9982049,0.00009693144,0.00003173366,0.00002888256,0.000003321617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9872832,0.0001598539,0.01039216,0.0001227911,0.00001594666,0.00003208352,0.0004352634,0.0001196765,0.001439089],"genre_scores_gemma":[0.9973003,0.00005935697,0.001374005,0.000007396375,0.000004111281,0.0000149483,0.0001835656,0.000006548752,0.001049748],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2586757,"threshold_uncertainty_score":0.51434,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01237543794578744,"score_gpt":0.2202846426509441,"score_spread":0.2079092047051566,"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."}}