{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003407,0.0001560749,0.0002195484,0.0001137312,0.000005681047,0.00001336584,0.00005499123,0.0001966357,0.0001945339],"category_scores_gemma":[7.528315e-7,0.0001105525,0.00002407044,0.0000213489,0.000009809894,0.00008097148,0.00002084771,0.00006081438,0.00007903996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007796433,"about_ca_system_score_gemma":0.000003377125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005611053,"about_ca_topic_score_gemma":0.0001441063,"domain_scores_codex":[0.9994917,0.00000891232,0.0001780768,0.0001241303,0.00008133311,0.0001158446],"domain_scores_gemma":[0.9997635,0.000002420108,0.00003042705,0.0001649399,0.00001368851,0.00002497435],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000002490531,0.000004826618,0.00002841742,0.0006425859,0.00003033662,0.000001964022,0.0005759216,0.01650937,0.0001847424,0.00002652149,0.981414,0.0005788375],"study_design_scores_gemma":[0.0002156613,0.00001434503,0.000007193195,0.0001542569,0.00002456881,0.00000259899,0.00001307899,0.02629494,0.0004371925,0.00005658836,0.972536,0.0002435538],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.000405805,0.0005501712,0.01538321,0.00002229576,0.0007380844,0.0003042346,0.00002956593,0.0001913272,0.9823753],"genre_scores_gemma":[0.01862103,0.0006869526,0.001779031,0.00000596609,0.000393339,0.0000131619,0.0001256655,0.0003254599,0.9780494],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.01821522,"threshold_uncertainty_score":0.45082,"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."}}