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Record W4411680857 · doi:10.4271/2025-01-5044

Minimum Sound Testing of Medium- and Heavy-Duty Electric Vehicles

2025· article· en· W4411680857 on OpenAlexaffabout
Vinay Sharma, Marc-André Larocque-Legros, Cole Weston, Martha Christenson

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2025
Typearticle
Languageen
FieldEngineering
TopicVehicle Noise and Vibration Control
Canadian institutionsGovernment of CanadaTransport Canada
Fundersnot available
KeywordsHeavy dutySound (geography)Automotive engineeringAcousticsComputer scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">The acoustic performance of seven vehicles was evaluated according to Canadian Motor Vehicle Safety Standard 141 (CMVSS 141), which governs minimum required sound levels for hybrid and electric vehicles with a gross vehicle weight rating (GVWR) of 4536 kg (10,000 lb) or less. To better understand the sound profiles of medium-duty electric vehicles (MDEVs) and heavy-duty electric vehicles (HDEVs), the sound emissions of two light-duty electric vehicles (LDEVs), one MDEV, three HDEVs, including an electric transit bus, and one heavy-duty internal combustion engine (HD ICE) vehicle were compared. The sound emissions of the MDEV and HDEVs were quieter than the HD ICE vehicle and comparable to that of the LDEVs equipped with auxiliary speakers. The MDEV with its auxiliary speaker turned off and all three HDEVs without auxiliary speakers met CMVSS 141 requirements in reverse gear and at speeds of 20 km/h and 30 km/h. The MDEV, though not subject to CMVSS 141, failed to meet the minimum sound requirement at stationary when its auxiliary speaker was deactivated. Similarly, the electric transit bus did not meet sound requirements at stationary and 10 km/h, highlighting the potential of acoustic vehicle alert systems (AVAS) in enhancing the detectability of quiet vehicles.</div></div>

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.978
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.011
GPT teacher head0.235
Teacher spread0.224 · 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.

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
Published2025
Admission routes2
Has abstractyes

Explore more

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