A data processing method for noise measurements of snowmobiles and their sub-systems on test bench
Bibliographic record
Abstract
A new method for processing and analyzing noise data from a snowmobile on a test bench is proposed and applied to compare the sound levels of two different CVT models. Because of the important dependency of the noise to the engine speed, the signal is analyzed through a relatively simple algorithm so as to be automatically associated to the engine speed, resulting in comparable noise levels at the same mean speed for both CVT models. Two indicators calculated from the unbiased variance are provided by this method and allow to conclude on noise differences between different CVT models. Those indicators are the confidence intervals (using Student's law) and the significance of differences coefficient (SD-coefficient), the latter being introduced and detailed for the first time in this study. The method is applied to test bench measurements and compared to pass-by noise measurements. As time averaging does not give good results on test bench, particularly as engine speed control is difficult, hence this approach to data processing is necessary to obtain results comparable to a pass-by test. Even if the absolute values on test bench are not exactly the same as pass-by values, the use of the data processing method is advantageous for comparing several CVT models and making predictions on the CVT pass-by noise reduction. Thereby the proposed methodology saves time (test bench measurements are faster than setting up and executing pass-by tests) and avoids problems and discrepancies caused by environmental conditions (snow quality/quantity, wind, piloting).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".