Noise Data from Snowmobile Pass-bys: The Significance of Frequency Content
Bibliographic record
Abstract
<div class="htmlview paragraph">This paper presents a summary of the results of noise measurements of various snow machines conducted by Harris Miller Miller &amp; 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.</div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".