{"id":"W4410321377","doi":"10.1007/978-981-96-1464-6_47","title":"Digital Twin Modeling of Pipeline Resonance Bending Fatigue Testing Machine Based on Data Driven Machine Learning","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in mechanical engineering","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Pipeline (software); Bending; Computer science; Artificial intelligence; Structural engineering; Machine learning; Engineering; Operating system","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":[0.0002635155,0.0003485336,0.0004535135,0.0003569181,0.0002448047,0.0006019361,0.0008604189,0.0005472038,0.002668503],"category_scores_gemma":[0.0005649331,0.0002950519,0.0005158967,0.0003218969,0.0002770986,0.0006846742,0.0004198572,0.0005012997,0.0004889481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005803179,"about_ca_system_score_gemma":0.0006520706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005392252,"about_ca_topic_score_gemma":0.00567232,"domain_scores_codex":[0.9998471,0.00001935949,0.000009403173,0.00004871759,0.0000610622,0.00001442533],"domain_scores_gemma":[0.999763,0.00008371178,0.00002379566,0.00002904095,0.00009029563,0.0000101298],"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":[0.00004848119,0.00002817938,0.000547641,0.00004650853,0.00001754112,0.00003490224,0.00003362969,0.9270769,0.004902629,0.003482379,0.0005689314,0.0632123],"study_design_scores_gemma":[4.291335e-7,0.000005002743,0.00005445188,9.239171e-7,0.000001595299,0.000003987507,9.753614e-7,0.9990985,0.0004081711,0.000255755,0.0001690371,0.000001135506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0285823,0.0002291259,0.966079,0.0001082663,0.00004271221,0.00002990393,0.0001022882,0.0008171304,0.00400928],"genre_scores_gemma":[0.8612254,0.0002332104,0.1293024,0.00006717753,0.00002371943,0.0001368765,0.0003178447,0.0001031635,0.008590164],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005392252,"threshold_uncertainty_score":0.01072174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03559804833948737,"score_gpt":0.2378785543254043,"score_spread":0.2022805059859169,"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."}}