{"id":"W7066531218","doi":"","title":"An Integrated Data-Driven Failure Prediction and Risk Management Approach for Water Mains","year":2023,"lang":"en","type":"dissertation","venue":"Spectrum Research Repository (Concordia University)","topic":"Electromagnetic Compatibility and Measurements","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cluster analysis; Risk management; Risk assessment; Mains electricity; Predictive modelling; Action plan; Analytics; Process (computing); Water resources; Failure mode and effects analysis","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001842612,0.001288212,0.001202561,0.002279139,0.0005611615,0.00165218,0.001632407,0.000912698,0.001686654],"category_scores_gemma":[0.003682186,0.0005710897,0.001450565,0.001245735,0.0003223197,0.001291276,0.001202844,0.001331087,0.0003415706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001235085,"about_ca_system_score_gemma":0.001871996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02635629,"about_ca_topic_score_gemma":0.02640226,"domain_scores_codex":[0.9992365,0.0001608804,0.00007077798,0.0002174998,0.0002156926,0.00009872105],"domain_scores_gemma":[0.9980898,0.0008622721,0.0002665672,0.0001001091,0.0005497331,0.0001315515],"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":[0.000109956,0.0002049231,0.0104359,0.0001000197,0.0001466089,0.0001727458,0.0001436227,0.9039163,0.001144687,0.002082966,0.001579785,0.07996251],"study_design_scores_gemma":[0.000002649089,0.00002028265,0.0007314293,0.000006295976,0.00001123437,0.00001145891,0.00003315626,0.9976981,0.0001532802,0.001136302,0.0001894618,0.000006292777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0901544,0.000430749,0.9034935,0.0007119162,0.00006759157,0.0002894702,0.000973775,0.001809781,0.002068902],"genre_scores_gemma":[0.7928301,0.0003037446,0.2028755,0.0001024252,0.00006608908,0.0003285736,0.001651813,0.00007813836,0.001763654],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02635629,"threshold_uncertainty_score":0.05240571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03229141772634479,"score_gpt":0.258689939581307,"score_spread":0.2263985218549622,"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."}}