{"id":"W2948238214","doi":"10.1080/1478422x.2019.1615741","title":"Dynamic risk management of assets susceptible to pitting corrosion","year":2019,"lang":"en","type":"article","venue":"Corrosion Engineering Science and Technology The International Journal of Corrosion Processes and Corrosion Control","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Canada Excellence Research Chairs, Government of Canada; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Pitting corrosion; Risk management; Corrosion; Control limits; Limit (mathematics); Bayesian probability; Process (computing); Reliability engineering; Risk analysis (engineering); Computer science; Forensic engineering; Econometrics; Environmental science; Materials science; Engineering; Control chart; Metallurgy; Business; Economics; Mathematics; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.001002534,0.0002516112,0.0004173416,0.0008268851,0.0001971873,0.0001367758,0.001017109,0.0001517894,0.00002486368],"category_scores_gemma":[0.000454628,0.000171275,0.00008111512,0.001335399,0.0002537243,0.0004118416,0.0002680184,0.0005123391,0.000006191539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009701662,"about_ca_system_score_gemma":0.00007974376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004744385,"about_ca_topic_score_gemma":0.000004873486,"domain_scores_codex":[0.9977939,0.0000161907,0.0006765248,0.0003160716,0.000893442,0.0003038455],"domain_scores_gemma":[0.9975572,0.00022583,0.0003399876,0.0002739917,0.00146714,0.000135833],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001643164,0.00005279283,0.01193272,0.0002636148,0.00002194491,0.0000120628,0.0001892685,0.0440919,0.9204625,0.0008963582,0.0001323575,0.02178015],"study_design_scores_gemma":[0.004480872,0.001876027,0.008502283,0.004464527,0.0005479788,0.0007909153,0.004055139,0.688172,0.2791063,0.003146023,0.003685619,0.001172367],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9040417,0.001832827,0.09090351,0.0009324754,0.001840903,0.0003011616,0.00001649648,0.00008960161,0.00004134682],"genre_scores_gemma":[0.9958787,0.002504815,0.001433885,0.0000713781,0.00002680491,0.00001014797,0.000001365586,0.00001905218,0.00005385296],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6440801,"threshold_uncertainty_score":0.6984389,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002958856726339247,"score_gpt":0.2154465499348694,"score_spread":0.2124876932085302,"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."}}