{"id":"W4413183006","doi":"10.2139/ssrn.5389685","title":"Physics-Informed Machine Learning Modeling and inferencer-in-The-Loop Based Real-Time Digital-Twin Emulation for a High-Speed Maglev Transportation System","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Sensorless Control of Electric Motors","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Maglev; Emulation; Loop (graph theory); Computer science; Control engineering; Simulation; Engineering; Electrical engineering; Psychology; Mathematics","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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0009359588,0.0004652631,0.0006237724,0.000404718,0.0001644703,0.0002866244,0.0003238049,0.0002869157,0.000002452903],"category_scores_gemma":[0.00008332951,0.0004313771,0.0002077884,0.0002739569,0.00001538043,0.0002908533,0.00001277353,0.002708249,0.000002002471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001821227,"about_ca_system_score_gemma":0.001342744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002761064,"about_ca_topic_score_gemma":0.0004028841,"domain_scores_codex":[0.99695,0.0000803898,0.0008204387,0.0003379858,0.0003721248,0.001439021],"domain_scores_gemma":[0.9989044,0.000361517,0.0002781582,0.0002338773,0.0001577189,0.00006430993],"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":[0.0001366183,0.00001804956,0.0003569973,0.0004700237,0.0002119691,0.000002759205,0.0002899,0.9774832,0.0003035075,0.006636445,0.000003519291,0.01408707],"study_design_scores_gemma":[0.001872944,0.0001177926,0.0001875299,0.0004334784,0.0001824732,0.00001186318,0.0002228965,0.9845089,0.00004699586,0.01203296,0.00001116577,0.0003709683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.564822,0.0009889969,0.4318365,0.0001159622,0.0002195365,0.001238268,0.00007250623,0.0002958091,0.0004103666],"genre_scores_gemma":[0.9979421,0.0007335416,0.0003499902,0.00001023115,0.0002486754,0.00006438261,0.0004293608,0.00006524997,0.0001565051],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.43312,"threshold_uncertainty_score":0.9998138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009592269270974424,"score_gpt":0.2212664301355966,"score_spread":0.2116741608646222,"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."}}