{"id":"W60800653","doi":"","title":"Probabilistic Modeling and Bayesian Inference of Metal-Loss Corrosion with Application in Reliability Analysis for Energy Pipelines","year":2014,"lang":"en","type":"article","venue":"Scholarship@Western (Western University)","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pipeline transport; Reliability (semiconductor); Corrosion; Probabilistic logic; Bayesian probability; Reliability engineering; Bayesian inference; Computer science; Inference; Frequentist inference; Environmental science; Engineering; Artificial intelligence; Materials science; Metallurgy","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003445956,0.000226823,0.0005026683,0.0005380984,0.00007616005,0.00004655522,0.0002745915,0.0001850503,0.000002294622],"category_scores_gemma":[0.000107177,0.000205953,0.0001349992,0.001035628,0.0001290922,0.0005181778,0.00006464593,0.0002303094,5.637528e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001065515,"about_ca_system_score_gemma":0.00001965818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008917541,"about_ca_topic_score_gemma":0.01058414,"domain_scores_codex":[0.9986959,0.0001151844,0.0003415676,0.0004384705,0.0001820601,0.0002268326],"domain_scores_gemma":[0.9989092,0.0002303178,0.00009782018,0.0004269427,0.000234484,0.000101248],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007059101,0.00002567533,0.6001503,0.0001838335,0.00007094631,8.983235e-7,0.0001102203,0.3981279,0.0005597423,0.0002737604,6.911719e-9,0.0004261053],"study_design_scores_gemma":[0.001452939,0.0002448125,0.5231255,0.0002479195,0.001868558,0.000005405616,0.0003024363,0.4617961,0.004789766,0.005272229,0.00007162537,0.0008227458],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6061732,0.00002406881,0.3935639,0.00002319639,0.00001658876,0.0001312057,0.00001179435,0.00004757574,0.000008465141],"genre_scores_gemma":[0.9993325,0.00004760615,0.0004813738,0.00001253816,0.00001624157,0.000004224644,0.00004116386,0.0000167906,0.00004749961],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3931594,"threshold_uncertainty_score":0.839852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03379122872598633,"score_gpt":0.2672186376778671,"score_spread":0.2334274089518807,"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."}}