Measurement of Noise and Vibration in Canadian Forces Armoured Vehicles
Bibliographic record
Abstract
Noise and whole-body vibration measurements were made in the following Canadian Forces vehicles: LAV III, Bison and M113A2 ADATS (air defence anti-tank system). Measurements were made at different crew positions while the vehicles were driven at different speeds over rough terrain and paved roads. The participants completed a questionnaire at the end of each measurement session on their reactions to the noise and vibration. Noise levels were as high as 115 dBA in the ADATS, 102 dBA in the Bison and 96 dBA in the LAV III, exceeding the Canada Labour Code exposure limit of 87 dBA for 8 h(1)). A communications headset was found to be sufficient to reduce the noise exposure to safe levels in most cases. The vector sum vibration magnitudes for the LAV III and Bison were relatively low during highway driving (0.3 m/s(2) for both vehicles) compared to rough terrain (0.71 and 1.36 m/s(2), respectively). The ADATS vibration increased with driving speed (0.62 m/s(2) at 8 km/h and 1.26 m/s(2) at 32 km/h). The questionnaire responses indicated that half the crewmembers had difficulty communicating in vehicle noise, but were generally unaffected physically by vibration. The latter result may have been due to the relatively short exposure duration.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".