Bibliographic record
Abstract
Measurements of diesel exhaust emissions (DEEs) were taken in the cabs of leading and trailing locomotives on 48 runs, under winter and summer conditions, on 9 different routes. The cab windows were kept open during the summer runs and closed during the winter runs. The average measurement duration was 9.5 hours. There was virtually no exposure to DEEs in the lead locomotives during winter or summer and very little in the trailing locomotives during winter. The average elemental carbon (EC) concentration in the trailing units of the summer trials was greater than or equal to the proposed American Conference of Governmental Industrial Hygienists' threshold limit value/time-weighted average of 20 µg/m3 on 26% of the runs, and was greater than or equal to 10 µg/m3 on 63%. The concentrations of the gaseous components (nitric oxide, nitrogen dioxide, and carbon monoxide) were from 10 to 20 times below their respective threshold limit values. Mean EC concentration was 2.9 µg/m3 (detection limit 2 µg/m3) during the winter runs and 17.1 µg/m3 during summer. DEEs appeared to be fairly uniformly distributed in the trailing cabs. Configuration of the locomotives had a major impact on EC concentration, with the mean concentration being nearly three times higher in the forward–backward mode than in the forward–forward mode. Descriptive statistics such as means, medians, standard deviations, and so forth, are provided. Various types of statistical comparisons are reported. 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 | 0.000 | 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".