Noise Data from Snowmobile Pass-bys: The Significance of Frequency Content
Bibliographic record
Abstract
This paper presents a summary of the results of noise measurements of various snow machines conducted by Harris Miller Miller & Hanson Inc. (HMMH) in 2002. Because the data were collected as part of an analysis including audibility and sound propagation over long distances in national parks, measurements included the frequency content of the snow machines as well as the A-weighted sound levels (dBA). Frequency data are given for some of the snow machine pass-bys at the SAE Clean Snowmobile Challenge 2002 and also for those measured under various operational conditions at Yellowstone National Park in February 2002. Measurements were conducted in substantial conformance with SAE J192. Comparisons are made of snow machines under acceleration and constant-speed conditions, and between those with two-stroke and four-stroke engines. The data show substantial differences in spectral content for some vehicles with similar A-weighted sound levels. A description of the significance of low-frequency tonal content on the audibility of noise in remote areas provides context for the spectral data presented.
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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 | 0.000 | 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 | 0.001 |
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".