{"id":"W2040891219","doi":"10.5539/mer.v2n2p108","title":"Grey Prediction on Rolling Bearing Friction Torque","year":2012,"lang":"en","type":"article","venue":"Mechanical Engineering Research","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Education Department of Henan Province; Henan University; Henan University of Science and Technology; National Natural Science Foundation of China","keywords":"Bearing (navigation); Friction torque; Torque; Residual; Reliability (semiconductor); A priori and a posteriori; Test data; Structural engineering; Engineering; Computer science; Artificial intelligence; Algorithm; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01615692,0.0001369331,0.0002039822,0.0006299665,0.0003202498,0.000213857,0.0006610133,0.0001683187,0.000228248],"category_scores_gemma":[0.008144705,0.0001147436,0.00009203082,0.001573614,0.00002500643,0.0004167467,0.0002079865,0.0007097896,0.002142762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002595785,"about_ca_system_score_gemma":0.00002700025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001882023,"about_ca_topic_score_gemma":0.000001459788,"domain_scores_codex":[0.9956324,0.0003461223,0.0005230752,0.0004502666,0.002333588,0.0007145095],"domain_scores_gemma":[0.9955754,0.00277095,0.00005631361,0.0008438918,0.0003927354,0.000360684],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009554443,0.0003798713,0.00324871,0.00003768408,0.00004974983,0.000004690962,0.000874473,0.05513867,0.1855009,0.7325299,0.002947472,0.0191923],"study_design_scores_gemma":[0.0009972774,0.0006195054,0.03222002,0.000286724,0.0000227947,0.00006420987,0.00116164,0.7200333,0.09811649,0.04008967,0.1055896,0.0007987499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6557072,0.0001219218,0.3362849,0.0005528933,0.001672256,0.0007746862,0.00001610156,0.0004338032,0.004436201],"genre_scores_gemma":[0.9973036,0.000003742889,0.00109581,0.00001422884,0.0007134289,0.0001632561,0.000003133764,0.00003061848,0.0006721663],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6924402,"threshold_uncertainty_score":0.9986342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2283160633658574,"score_gpt":0.4305040870720327,"score_spread":0.2021880237061753,"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."}}