A field test study of airborne wear particles from a running regional train
Bibliographic record
Abstract
Inhalable airborne particles have inverse health effects. In railways, mechanical brakes, the wheel–rail contact, current collectors, ballast, sleepers, and masonry structures yield particulate matter. Field tests examined a Swedish track using a train instrumented with particle measurement devices, brake pad temperature sensors, and speed and brake sensors. The main objective of this field test was to study the characteristics of particles generated from disc brakes on a running train with an on-board measuring set-up. Two airborne particle sampling points were designated, one near a pad–rotor disc brake contact and a second under the frame, not near a mechanical brake or the wheel–rail contact; the numbers and size distributions of the particles detected were registered and evaluated under various conditions (e.g. activating/deactivating electrical brakes or negotiating curves). During braking, three speed/temperature-dependent particle peaks were identified in the fine region, representing particles 280, 350, and 600 nm in diameter. In the coarse region, a peak was discerned for particles 3–6 µm in diameter. Effects of brake pad temperature on particle size distribution were also investigated. Results indicate that the 280-nm peak increased with increasing temperature, and that electrical braking significantly reduced airborne particle numbers. Field emission scanning electron microscope images captured particles sizing down to 50 nm. The inductively coupled plasma mass spectrometry results indicated that Fe, Cu, Zn, Al, Ca, and Mg were the main elements constituting the particles.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".