Characterization of vibration and noise exposure in Canadian Forces armored vehicles
Bibliographic record
Abstract
A study to characterize the vibration and noise exposure in several Canadian Forces (CF) armored vehicles is in progress. Measurements of whole-body vibration and ambient noise levels are being made in the LAV III, Bison, Coyote, and M113 vehicles at three different positions: driver, crew commander, and passenger bench (or navigator seat in the case of the Coyote). The measurements are being made while the vehicles are idling, driven over rough terrain, and driven at a high speed on paved highways. There are several standards that provide guidance on the measurement and assessment of whole-body vibration, but they are difficult to implement in practice, particularly in adverse environments. The whole-body vibration measurements in this study are particularly difficult to interpret in the case of the crew commander, who often stands on the seat, and the passenger, who is seated but unrestrained by a seatbelt. The preliminary results-suggest, that according to the International Organization for Standardization guidelines (ISO 2631-1:1997), there may be potential health risks for the driver and passenger after driving on rough terrain for less than 10 min. Noise levels were as high as 100 dBA during high-speed highway driving.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".