{"id":"W4389540951","doi":"10.17118/11143/20944","title":"On scaling design and dynamic response prediction of rotorsystems","year":2023,"lang":"en","type":"article","venue":"","topic":"Magnetic Bearings and Levitation Dynamics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Science and Technology Major Project; National Natural Science Foundation of China","keywords":"Scaling; Computer science; Rotor (electric); Control theory (sociology); Dynamic scaling; Control engineering; Engineering; Mathematics; Artificial intelligence; Mechanical engineering","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":[0.0003857993,0.0004941963,0.0003792032,0.0003085792,0.0001997358,0.0002580699,0.0003157378,0.000451726,0.001197312],"category_scores_gemma":[0.001352906,0.0002287781,0.00030886,0.0001754286,0.0002989202,0.0003803366,0.0002117216,0.0002533213,0.0002171434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003973061,"about_ca_system_score_gemma":0.0003461259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002543519,"about_ca_topic_score_gemma":0.001366584,"domain_scores_codex":[0.9998425,0.00004417858,0.000007250062,0.00004022758,0.00005096929,0.0000147783],"domain_scores_gemma":[0.9995512,0.0002118299,0.00006134936,0.00004232809,0.0001177549,0.00001547939],"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":[0.00005904698,0.00005286908,0.001485359,0.000100975,0.000009810738,0.0000570441,0.00005062593,0.9592384,0.01616673,0.001193668,0.000268797,0.02131672],"study_design_scores_gemma":[0.000002263638,0.0000240498,0.0001793671,0.0000013379,0.000001428347,0.00000406273,0.000002288402,0.9986553,0.0009647388,0.00007989095,0.00008379721,0.000001357803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3614805,0.0002993689,0.6326122,0.0001240119,0.00003918665,0.0001101256,0.00008381292,0.0007179227,0.004532883],"genre_scores_gemma":[0.9866845,0.00005024542,0.01287663,0.000008144835,0.000003514595,0.00003395074,0.00003477302,0.00001331448,0.0002950095],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002543519,"threshold_uncertainty_score":0.005057395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01240388464954753,"score_gpt":0.2106223082885889,"score_spread":0.1982184236390414,"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."}}