{"id":"W4312307603","doi":"10.1115/ipc2022-87320","title":"Implementation of API 1183 Recommended Practice for Reliability-Based Assessment of Dents in Liquid Pipelines","year":2022,"lang":"en","type":"article","venue":"","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Reliability (semiconductor); Probabilistic logic; Pipeline (software); Pipeline transport; Computer science; Engineering; Best practice; Reliability engineering; Artificial intelligence; Mechanical engineering","routes":{"ca_aff":false,"ca_fund":false,"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":[0.0007868498,0.00009124826,0.0002372436,0.0001528189,0.00003893572,0.00000520138,0.0001362533,0.00003755239,0.0006730197],"category_scores_gemma":[0.0001105832,0.00008601344,0.0001239242,0.0003392237,0.00001745804,0.0001181804,0.00003709403,0.0001638918,2.154985e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002022085,"about_ca_system_score_gemma":0.00005424361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001129744,"about_ca_topic_score_gemma":0.0004605883,"domain_scores_codex":[0.9988111,0.000119107,0.0005777928,0.0001464011,0.0002191115,0.0001265448],"domain_scores_gemma":[0.9991025,0.000411253,0.0001145066,0.0001983059,0.0001512063,0.00002224238],"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":[0.0005615969,0.0007189086,0.01792349,0.001470457,0.000311325,0.000001458808,0.0009459686,0.9262899,0.0385514,0.003168114,0.001592857,0.008464581],"study_design_scores_gemma":[0.004041622,0.001789725,0.007678826,0.00004249304,0.000361503,0.000003547922,0.02927862,0.7001156,0.2456815,0.002718756,0.007762936,0.0005249008],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9765748,0.00003356811,0.02045294,0.001450329,0.0002585848,0.000553593,0.00009902615,0.00004819702,0.0005289999],"genre_scores_gemma":[0.967988,0.00001334847,0.03169151,0.00008166672,0.00001050792,0.0001173122,0.00007401963,0.000009561189,0.00001407719],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2261743,"threshold_uncertainty_score":0.7369093,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0175229307168762,"score_gpt":0.3512237192538557,"score_spread":0.3337007885369795,"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."}}