{"id":"W4386892956","doi":"10.1016/j.ress.2023.109672","title":"Improving failure modeling for gas transmission pipelines: A survival analysis and machine learning integrated approach","year":2023,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Hong Kong Polytechnic University","keywords":"Censoring (clinical trials); Pipeline (software); Pipeline transport; Reliability engineering; Reliability (semiconductor); Computer science; Machine learning; Covariate; Engineering; Data mining; Artificial intelligence; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001848127,0.0005460611,0.00109016,0.0006607019,0.0002794263,0.0001147432,0.0002743589,0.0004125512,0.000009941173],"category_scores_gemma":[0.0004090852,0.0004661111,0.0006272403,0.002682264,0.00004148934,0.0002190993,0.0000484705,0.0007907087,0.000005162302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003701374,"about_ca_system_score_gemma":0.0000297477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007718463,"about_ca_topic_score_gemma":0.00005779102,"domain_scores_codex":[0.9970655,0.0001177245,0.001024469,0.0007692472,0.0003633855,0.000659639],"domain_scores_gemma":[0.9984779,0.000456483,0.00007439511,0.0005079054,0.0002335277,0.000249752],"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":[0.00004266304,0.00001056927,0.0005830696,0.00318197,0.000511672,0.000001650754,0.0003487311,0.9882309,0.003422933,0.0001261895,0.000005736118,0.003533907],"study_design_scores_gemma":[0.0004145908,0.00002864527,0.0002269107,0.0001424106,0.0007578093,0.00000781949,0.0009740285,0.9958981,0.0003609588,0.00002070353,0.0006652027,0.0005027769],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1504504,0.0003797656,0.84567,0.00007871089,0.0002486994,0.0005551946,0.0001014992,0.002462442,0.00005318746],"genre_scores_gemma":[0.9807721,0.000176051,0.01817271,0.000002165244,0.000123014,0.00009823563,0.000507587,0.00009347108,0.00005464865],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8303217,"threshold_uncertainty_score":0.999779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01002705680750701,"score_gpt":0.1977532676427102,"score_spread":0.1877262108352032,"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."}}