{"id":"W4379033730","doi":"10.1109/tim.2023.3279910","title":"Data Modeling Techniques for Pipeline Integrity Assessment: A State-of-the-Art Survey","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Research Council Canada","keywords":"Pipeline (software); Integrity management; Pipeline transport; Reliability engineering; Computer science; Data integrity; Data modeling; Engineering; Process (computing); Systems engineering; Risk analysis (engineering); Computer security; Software engineering","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":[0.00101083,0.0001204333,0.0001552392,0.0001266321,0.000143828,0.00003001919,0.0001698551,0.00005395808,0.00001444267],"category_scores_gemma":[0.00001811673,0.00009405287,0.00006986419,0.0003032856,0.00004038741,0.000159393,0.000003013727,0.0002287568,0.000002177084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001124865,"about_ca_system_score_gemma":0.00004023852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001570204,"about_ca_topic_score_gemma":0.00161105,"domain_scores_codex":[0.9989446,0.00006796931,0.0003294223,0.0001938585,0.0003337884,0.0001304055],"domain_scores_gemma":[0.9993854,0.00005302769,0.00003713858,0.0003060403,0.0001778561,0.00004057354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001502921,0.0001829131,0.000333195,0.0006339882,0.0005760703,2.712196e-7,0.0005651567,0.3727186,0.02735171,0.00008547842,0.002824511,0.5945778],"study_design_scores_gemma":[0.000329088,0.00003968529,0.0007024722,0.00007932401,0.00008296764,7.772744e-7,0.0001094013,0.937929,0.05965555,0.0004410339,0.0005019501,0.0001287081],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0792858,0.00001554261,0.9186537,0.0002677682,0.0004945092,0.0004295563,0.0006247459,0.0001658315,0.00006251167],"genre_scores_gemma":[0.9970836,0.0002780786,0.002369983,0.00003789272,0.00001046528,0.00006572012,0.00008260035,0.00001317976,0.00005849807],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9177978,"threshold_uncertainty_score":0.3835365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1317487025558981,"score_gpt":0.3296049731064006,"score_spread":0.1978562705505025,"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."}}