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Passing Ability Evaluation and Impact Factors Analysis of Articulated Vehicle with 6×4 Tractor

2015· article· en· W2005190750 on OpenAlexaff
H.W. Zhang, H. Zhang, Jun Qi Yang

Bibliographic record

VenueApplied Mechanics and Materials · 2015
Typearticle
Languageen
FieldEngineering
TopicVehicle Dynamics and Control Systems
Canadian institutionsMinistry of Transportation of Ontario
Fundersnot available
KeywordsTractorAxleAutomotive engineeringEngineeringTrailerTest (biology)Articulated vehicleTest dataSimulationStructural engineeringTruck

Abstract

fetched live from OpenAlex

Passing Ability of articulated vehicles is tested and analyzed, which are combined of different 6×4 tractors and different loaded semitrailers. Test methods and test procedure is designed and developed. 90 times of test data are collected in ‘3rd Swap Trailer Transport Recommended Vehicles Test’. Articulated vehicles are combined of 25 different 6×4 tractors and 21 different loaded semitrailers. Test data are statistically analyzed and passing ability is evaluated according to the requirements of GB 1589-2004. Linear regression is used to study how tractor front overhang, distance between center of tractor fifth wheel and tractor first axle and semitrailer first wheel space impact passing ability. The results can serve as both advices in improving articulated vehicle and data references in developing and amending standards.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.951
Threshold uncertainty score0.333

Codex and Gemma teacher scores by category

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

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 head0.014
GPT teacher head0.230
Teacher spread0.216 · 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 teacher head, 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

Citations0
Published2015
Admission routes1
Has abstractyes

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