MétaCan
Menu
Back to cohort
Record W2040891219 · doi:10.5539/mer.v2n2p108

Grey Prediction on Rolling Bearing Friction Torque

2012· article· en· W2040891219 on OpenAlexvenueno aff
Xintao Xia, Tao Ma

Bibliographic record

VenueMechanical Engineering Research · 2012
Typearticle
Languageen
FieldDecision Sciences
TopicGrey System Theory Applications
Canadian institutionsnot available
FundersEducation Department of Henan ProvinceHenan UniversityHenan University of Science and TechnologyNational Natural Science Foundation of China
KeywordsBearing (navigation)Friction torqueTorqueResidualReliability (semiconductor)A priori and a posterioriTest dataStructural engineeringEngineeringComputer scienceArtificial intelligenceAlgorithmPhysics

Abstract

fetched live from OpenAlex

The rolling bearing friction torque is forecasted by means of the grey dynamic model GM (1, 1). Residual test and posteriori error test are conducted to verify the reliability of the results of prediction. The experiment shows that the method proposed has the high precision and satisfy the engineering demand.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.228
GPT teacher head0.431
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueMechanical Engineering ResearchSame topicGrey System Theory ApplicationsFrench-language works237,207