{"id":"W3119241231","doi":"10.1016/j.ress.2021.107438","title":"Quantitative assessment of leakage orifices within gas pipelines using a Bayesian network","year":2021,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":72,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Fundamental Research Funds for the Central Universities; Key Technologies Research and Development Program; PetroChina Innovation Foundation; Key Technology Research and Development Program of Shandong; National Natural Science Foundation of China","keywords":"Leakage (economics); Body orifice; Pipeline transport; Orifice plate; Engineering; Computer science; Reliability engineering; Mechanics; Mechanical engineering; Physics","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":[0.002210097,0.0006650815,0.0007218727,0.00192829,0.0005103119,0.001282677,0.0009266152,0.001341883,0.0008947716],"category_scores_gemma":[0.006858654,0.0007967975,0.0007861471,0.0009221516,0.0008670209,0.002117307,0.0009582507,0.0006810303,0.000120068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001338068,"about_ca_system_score_gemma":0.0009723702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007710576,"about_ca_topic_score_gemma":0.005226434,"domain_scores_codex":[0.9991581,0.0002730107,0.00004635279,0.0002167502,0.0002474023,0.0000584187],"domain_scores_gemma":[0.9952452,0.003554151,0.0006237098,0.0001205768,0.0003660001,0.0000904499],"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":[0.00007808014,0.00002757969,0.003082345,0.00002850916,0.00002559242,0.00002906037,0.00002818468,0.9847513,0.001773631,0.001723515,0.00005898943,0.008393277],"study_design_scores_gemma":[0.000002637088,0.00001153707,0.0008934267,0.000003698518,0.000007222876,0.000007766112,0.00000450841,0.997741,0.0003187142,0.0009709363,0.00003233961,0.000006147779],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2168074,0.0002499002,0.780948,0.0001640642,0.000008868421,0.0000563209,0.0001877453,0.0003172923,0.001260375],"genre_scores_gemma":[0.95744,0.0001262457,0.04163297,0.00002069831,0.00001172049,0.00004070652,0.0001435576,0.00002306585,0.0005609948],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007710576,"threshold_uncertainty_score":0.01533139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04871725392657326,"score_gpt":0.3558100870375846,"score_spread":0.3070928331110113,"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."}}