{"id":"W2997480570","doi":"10.1002/9781119300762.wsts0175","title":"Pipeline Failure and Deterioration Models","year":2019,"lang":"en","type":"other","venue":"Encyclopedia of Water","topic":"Water Systems and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia; University of Regina","funders":"","keywords":"Pipeline (software); Inference; Predictive modelling; Computer science; Bayesian inference; Bayesian probability; Reliability engineering; Economic shortage; Statistical model; Data mining; Engineering; Machine learning; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001426983,0.001613428,0.001287348,0.0023885,0.0006229361,0.001620392,0.002813712,0.002386175,0.009683121],"category_scores_gemma":[0.003419263,0.000557304,0.001534977,0.002366601,0.001222326,0.001626065,0.001194373,0.001784633,0.002498757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002376328,"about_ca_system_score_gemma":0.001654547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05853876,"about_ca_topic_score_gemma":0.02183512,"domain_scores_codex":[0.9991468,0.0001699306,0.00004939412,0.0002589991,0.0001955277,0.0001792291],"domain_scores_gemma":[0.998852,0.0004455945,0.0002414513,0.00006637834,0.0003512707,0.00004321946],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003222068,0.00002194393,0.002166737,0.00009311608,0.00002626146,0.0001044492,0.00005337317,0.9550536,0.0003054303,0.02586027,0.003592904,0.01268973],"study_design_scores_gemma":[0.000006604247,0.00001937994,0.001125367,0.00002183847,0.00001773825,0.00004256906,0.00001538911,0.9851086,0.0001329339,0.01023832,0.003258153,0.00001311286],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.098254,0.005885228,0.8022985,0.002800467,0.000479938,0.0002384415,0.011555,0.002125305,0.07636312],"genre_scores_gemma":[0.886862,0.003622211,0.01812129,0.0002058163,0.0002247735,0.0003135643,0.005966085,0.0001810738,0.08450326],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.05853876,"threshold_uncertainty_score":0.1163961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004597953900989318,"score_gpt":0.1671714019514471,"score_spread":0.1625734480504578,"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."}}