{"id":"W4312804703","doi":"10.1115/ipc2022-87093","title":"Estimating Pipeline Probability of Failure Due to External Interference Damage Using Machine Learning Algorithms Trained on In-Line Inspection Data","year":2022,"lang":"en","type":"article","venue":"","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pipeline (software); Interference (communication); Reliability engineering; Computer science; Pipeline transport; Limit (mathematics); Engineering; Algorithm; Risk analysis (engineering); Mathematics; Mechanical engineering; Telecommunications","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":[0.0006916858,0.0001788588,0.0003391279,0.0002410008,0.0001333838,0.00002683633,0.0004541909,0.00005367706,0.0004471215],"category_scores_gemma":[0.0003786385,0.000161844,0.0000608317,0.0006902345,0.00003973334,0.0001860036,0.0003512946,0.0008777946,0.000002245144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002344964,"about_ca_system_score_gemma":0.00001898533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002185967,"about_ca_topic_score_gemma":0.001451024,"domain_scores_codex":[0.9985017,0.0001311009,0.0005238937,0.0003807982,0.0002584283,0.000204109],"domain_scores_gemma":[0.9992478,0.0001074551,0.00006675843,0.0004652222,0.00005288778,0.0000598536],"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":[0.00003939708,0.00005539766,0.0008299464,0.0000864368,0.00001940932,0.000007939067,0.0005587587,0.9736314,0.01697347,0.00003593144,0.00001112303,0.00775078],"study_design_scores_gemma":[0.0002265092,0.0001275959,0.001316213,0.00006784827,0.0000214544,0.00002159218,0.0003422802,0.9932915,0.004014921,0.000384003,0.00002119684,0.0001648871],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7894052,0.0000222555,0.2097468,0.0001510959,0.0001865582,0.0001893966,0.00006281009,0.0001575599,0.00007839149],"genre_scores_gemma":[0.913867,0.000001219289,0.08589453,0.00002261164,0.00006324282,0.000008832598,0.00008370923,0.00001619232,0.00004267162],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1244618,"threshold_uncertainty_score":0.6599806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04636738519666159,"score_gpt":0.2840302256588775,"score_spread":0.2376628404622159,"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."}}