Bibliographic record
Abstract
Personal and area noise dosimetry measurements were taken in the cabs of leading and trailing locomotives on 48 trips, under winter and summer conditions, on 9 different routes. The mean equivalent sound level (LEQ, 3 dB exchange rate, 50 dBA threshold) of the engineers and conductors was 84 dBA during winter and 88 dBA during summer. The corresponding time-weighted average levels (LTWA, 5 dB exchange rate, 80 dBA threshold) were 80 and 84 dBA respectively. The LEQ of 56% of the engineers sampled was ≥85 dBA and of 13% was ≥90 dBA. Plots of LEQ time history show that under normal operating conditions LEQ reaches its steady-state value in about 3 hours. The mean noise levels in the trailing cabs were lower than the personal exposure levels of the engineers and conductors. The mean LEQ on the engineer and conductor sides was 80 dBA during winter, and 85 dBA during summer. Locomotive configuration has a significant effect on the noise levels in the trailing cab. The forward–backward configuration resulted in higher noise levels than the forward–forward configuration. Octave and one-third octave band spectra taken during a variety of locomotive operating conditions are presented. The octave band centered at 31.5 Hz contains nearly 46% of the acoustical energy, and those centered at and below 250 Hz contain nearly 99% of the acoustical energy. Wheel-rail interaction appears to be the predominant source of the low frequency noise. Recommendations for controlling exposure are made.
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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 | 0.000 |
| 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) | 0.002 | 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 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".