{"id":"W2997063475","doi":"10.28999/2514-541x-2019-3-2-92-104","title":"Integrity Analysis of Dented Pipelines using Artificial Neural Networks","year":2019,"lang":"en","type":"article","venue":"Pipeline Science and Technology","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Finite element method; Pipeline (software); Pipeline transport; Computer science; Engineering; Structural engineering; Artificial intelligence; Mechanical engineering","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":[0.0004244243,0.0001314136,0.000398544,0.001381962,0.00008551899,0.00002901651,0.0003316554,0.0001894729,0.00006281346],"category_scores_gemma":[0.0002276813,0.0001052922,0.00008639028,0.007086808,0.0008273375,0.0001681706,0.0001018962,0.0003839594,0.000002509292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004161023,"about_ca_system_score_gemma":0.0000263747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001601169,"about_ca_topic_score_gemma":0.0001660307,"domain_scores_codex":[0.9988317,0.00001186439,0.0003551195,0.0002937319,0.0002244015,0.0002831845],"domain_scores_gemma":[0.9991305,0.00004473316,0.0000590568,0.0003183337,0.0003972756,0.00005009291],"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":[0.000019177,0.00004684099,0.06381418,0.00005783291,0.000348728,0.000003473755,0.0001031352,0.7108997,0.166919,0.006148924,0.00003394249,0.05160514],"study_design_scores_gemma":[0.00005199794,0.00001904362,0.001192173,0.000007308132,0.0002729725,0.000005687729,0.0002880039,0.9844162,0.01264929,0.0009537243,0.00002981541,0.0001138184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9876691,0.0003063967,0.01116889,0.0002662264,0.0002733421,0.00008115471,0.000005691972,0.0001477554,0.00008148079],"genre_scores_gemma":[0.9990439,0.0000493645,0.0008330811,0.00002158726,0.00002940777,0.000001288275,0.000003896693,0.000005362959,0.00001215334],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2735165,"threshold_uncertainty_score":0.4293691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01609758882024426,"score_gpt":0.2644924033567597,"score_spread":0.2483948145365155,"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."}}