{"id":"W4297916564","doi":"10.2118/210406-ms","title":"Failure Pressure Prediction of Defective Pipeline Using Finite Element Method and Machine Learning Models","year":2022,"lang":"en","type":"article","venue":"SPE Annual Technical Conference and Exhibition","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Pipeline transport; Pipeline (software); Artificial neural network; Machine learning; Artificial intelligence; Finite element method; Computer science; Approximation error; Computation; Failure mode and effects analysis; Engineering; Reliability engineering; Structural engineering; Algorithm; Mechanical engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0003274144,0.0001050171,0.000189949,0.00008236577,0.0001431144,0.00001711917,0.00003700041,0.0000833599,0.00007418606],"category_scores_gemma":[0.00005122638,0.00009674927,0.00003762859,0.0001327869,0.00006644031,0.0001799085,0.0001019485,0.0004408033,9.88051e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002698554,"about_ca_system_score_gemma":0.000008766777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002143562,"about_ca_topic_score_gemma":0.00005115047,"domain_scores_codex":[0.9992463,0.00008753749,0.0002033218,0.000189608,0.0001661952,0.0001070643],"domain_scores_gemma":[0.9996787,0.00007079857,0.00004095602,0.00006976492,0.00009789084,0.00004189601],"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":[0.00009522773,0.00004612055,0.0009463092,0.0003461174,0.0001331984,0.000003009872,0.001678046,0.9191095,0.05294752,0.008760083,0.0001545686,0.01578031],"study_design_scores_gemma":[0.0001886249,0.0001641463,0.0003805486,0.00003409243,0.000132256,0.00002345481,0.0008856553,0.980082,0.00220276,0.01524964,0.0005520316,0.0001047345],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2509981,0.001353923,0.7457252,0.0003086843,0.00004722813,0.0003072693,0.0005868092,0.0002081404,0.0004646242],"genre_scores_gemma":[0.9958626,0.0003189487,0.003700257,0.00001549217,0.00001930181,0.00001118891,0.00004847193,0.000007176649,0.00001655183],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7448645,"threshold_uncertainty_score":0.3945321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03319174325465715,"score_gpt":0.260050950798046,"score_spread":0.2268592075433889,"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."}}