Minimum Sound Testing of Medium- and Heavy-Duty Electric Vehicles
Bibliographic record
Abstract
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.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".