{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006458464,0.0001171629,0.0001287898,0.000108656,0.00003644231,0.0004021898,0.0002847777,0.00007513173,0.00001946676],"category_scores_gemma":[0.00002943252,0.0001242935,0.00005168283,0.0004469449,0.00002395471,0.00128249,0.00001958832,0.00008916091,0.0003603025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002528776,"about_ca_system_score_gemma":0.00001222695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004926339,"about_ca_topic_score_gemma":0.000006692598,"domain_scores_codex":[0.999324,0.000005746452,0.0001924257,0.0001090426,0.0001231864,0.0002456002],"domain_scores_gemma":[0.9994206,0.0002744207,0.00001584088,0.0001495366,0.00007738298,0.00006221961],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005838199,0.0000499374,0.01745071,0.0007216054,0.0002634406,0.00001857844,0.000474342,0.2182383,0.0005602201,0.0003207457,0.6825892,0.07925449],"study_design_scores_gemma":[0.004035073,0.000129247,0.2334192,0.0002398138,0.00003429701,0.00002424897,0.0005517137,0.09604229,0.03006614,0.003715855,0.6295576,0.002184527],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9010849,0.00002760956,0.02080872,0.00008013013,0.00257927,0.000617023,0.001628801,0.002063751,0.07110976],"genre_scores_gemma":[0.9987548,0.000006873192,0.00001754998,0.0000474818,0.0001059249,0.00008528191,0.0002446414,0.00004134525,0.0006961091],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2159685,"threshold_uncertainty_score":0.5068541,"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."}}