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Record W4297150501 · doi:10.23977/jemm.2022.070303

Overview of Spindle-Coupled Road Simulation Test Method for Vehicle Structure Durability

2022· article· en· W4297150501 on OpenAlexvenueno aff
Guang Yang, Zhiqiang Zhao, Niu Zhihui, Yongzhe Wang

Bibliographic record

VenueJournal of Engineering Mechanics and Machinery · 2022
Typearticle
Languageen
FieldEngineering
TopicEngineering Applied Research
Canadian institutionsnot available
Fundersnot available
KeywordsAutomotive industryDurabilityAutomotive engineeringTest (biology)EngineeringProcess (computing)Quality (philosophy)PaceTest methodManufacturing engineeringComputer scienceAerospace engineering

Abstract

fetched live from OpenAlex

The durability of vehicle structure is one of the important performances to evaluate the quality of automobiles, and it has been paid more and more attention by automobile manufacturers and consumers. In the whole vehicle development process, automotive structural durability testing is essential. With the advantages of high test accuracy and short test cycle, road simulation test has become an effective test method in the vehicle development stage. Among them, the spindle-coupled road simulation test can most comprehensively reproduce the excitation of the road to the vehicle. The relevant test technology is synchronized with the rapid development of automotive products and keeps pace with the times, becoming one of the most deeply cultivated test technologies in the industry.

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)0.002
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0030.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.005

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 head0.019
GPT teacher head0.287
Teacher spread0.269 · 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 designBench or experimental
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

Citations1
Published2022
Admission routes1
Has abstractyes

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