{"id":"W3091614084","doi":"10.1016/j.ress.2020.107262","title":"Bayesian network model for predicting probability of third-party damage to underground pipelines and learning model parameters from incomplete datasets","year":2020,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bayesian network; Pipeline (software); Pipeline transport; Maximization; Context (archaeology); Computer science; Bayesian inference; Data mining; Bayesian probability; Artificial intelligence; Machine learning; Engineering; Mathematical optimization; Mathematics; Geology","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001188028,0.0004996177,0.001051726,0.00007166321,0.0002068117,0.0000710432,0.0004003521,0.0002940432,0.000003237706],"category_scores_gemma":[0.001238016,0.0004766763,0.0002770546,0.0004603226,0.00008179615,0.0002934764,0.0001639385,0.0007008698,0.00000195572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003087255,"about_ca_system_score_gemma":0.00004801596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002961204,"about_ca_topic_score_gemma":0.0000696198,"domain_scores_codex":[0.9968362,0.0001271471,0.001286012,0.0008327067,0.0003441366,0.0005737545],"domain_scores_gemma":[0.9975743,0.001010921,0.0001402931,0.0007039195,0.0001565157,0.0004140725],"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":[0.0001127574,0.00001196004,0.001570301,0.002668277,0.00015195,6.007938e-7,0.0009768831,0.9923808,0.001531678,0.000277464,0.0001287473,0.0001886056],"study_design_scores_gemma":[0.0003603444,0.00006368633,0.0007807955,0.0003177906,0.0001834794,0.000001493875,0.0002059524,0.9965571,0.0002892548,0.0006856786,0.0001102409,0.0004441717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3539259,0.0001052754,0.6433215,0.0002127981,0.0001433208,0.0007980868,0.0009036589,0.0005634176,0.00002606109],"genre_scores_gemma":[0.8680379,0.00001775636,0.1313777,0.00003621431,0.0001367968,0.00006750852,0.0002585351,0.00006322678,0.000004366205],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5141121,"threshold_uncertainty_score":0.9997685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01906379145316559,"score_gpt":0.2142740169049427,"score_spread":0.1952102254517771,"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."}}