{"id":"W4312778523","doi":"10.1115/ipc2022-87211","title":"Machine Learning-Based Severity Assessment of Pipeline Dents","year":2022,"lang":"en","type":"article","venue":"","topic":"Hydrogen embrittlement and corrosion behaviors in metals","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Stantec (Canada)","funders":"","keywords":"Pipeline (software); Finite element method; Reliability (semiconductor); Computer science; Reliability engineering; Machine learning; Artificial intelligence; Engineering; Structural 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008008624,0.0007431931,0.0004905407,0.001887601,0.0001724661,0.0005364784,0.0005748185,0.0007494511,0.001352801],"category_scores_gemma":[0.00291721,0.0002946468,0.0005189026,0.0004639589,0.0002679006,0.0004722621,0.000467893,0.0004038026,0.0004649577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00042146,"about_ca_system_score_gemma":0.0003205638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002198484,"about_ca_topic_score_gemma":0.003276575,"domain_scores_codex":[0.999696,0.000040803,0.00003199977,0.00006243402,0.0001404751,0.00002816963],"domain_scores_gemma":[0.9985965,0.0004537927,0.0003118628,0.000131272,0.000451802,0.00005477192],"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":[0.0003025811,0.0002835043,0.04065497,0.000144799,0.00006143197,0.0001831272,0.0000891232,0.6829888,0.0280643,0.0005941672,0.00163177,0.2450015],"study_design_scores_gemma":[0.000002874535,0.0000524975,0.008511315,0.000006902098,0.00000602651,0.0000317531,0.0000114217,0.9878711,0.003237243,0.0001414123,0.00011915,0.000008404777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6879984,0.0002393688,0.3062762,0.0001359502,0.00003858073,0.0001779981,0.0006070107,0.001491753,0.003034802],"genre_scores_gemma":[0.9765614,0.00004494952,0.02199029,0.00001830498,0.000008477017,0.00002879011,0.0004138869,0.00001574385,0.0009180771],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002198484,"threshold_uncertainty_score":0.004525542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01948574680335262,"score_gpt":0.3019826794312591,"score_spread":0.2824969326279064,"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."}}