{"id":"W4387886049","doi":"10.1109/access.2023.3327042","title":"Digital Twin for Railway: A Comprehensive Survey","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Ottawa","funders":"","keywords":"Digital transformation; Automation; Computer science; Context (archaeology); The Internet; Industry 4.0; Emerging technologies; Systems engineering; Data science; Engineering; World Wide Web","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":[0.00119313,0.0005650026,0.0004504191,0.009473414,0.000656188,0.003692985,0.000783295,0.001194517,0.01259729],"category_scores_gemma":[0.004342745,0.0003855336,0.0004986088,0.01582952,0.0005836277,0.00713515,0.001521291,0.001224134,0.004325967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009666148,"about_ca_system_score_gemma":0.001727222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002802776,"about_ca_topic_score_gemma":0.003282398,"domain_scores_codex":[0.9989808,0.0001384512,0.0001387148,0.0001653626,0.0004695984,0.0001069971],"domain_scores_gemma":[0.996278,0.002122564,0.0003211693,0.0001925896,0.0008445798,0.0002411509],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002952669,0.00004734371,0.004228141,0.005156481,0.00002262712,0.0002266905,0.001104213,0.000636574,0.001180051,0.04063686,0.05155287,0.8951786],"study_design_scores_gemma":[0.000001245814,0.00002665327,0.003485453,0.002134302,0.00002128544,0.0006037393,0.0009015689,0.0002801756,0.0004821631,0.002527697,0.9895181,0.00001753673],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.02403943,0.7733762,0.01718266,0.006076591,0.001127426,0.0001033296,0.002197058,0.0007023115,0.175195],"genre_scores_gemma":[0.06110834,0.9054669,0.008123357,0.001857442,0.0005406451,0.00004647468,0.00298417,0.0002427058,0.01962995],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01259729,"threshold_uncertainty_score":0.04214215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09223648655573961,"score_gpt":0.3152457462614958,"score_spread":0.2230092597057561,"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."}}