Low audibility of trains may contribute to increased collisions with wildlife
Bibliographic record
Abstract
Train collisions with wildlife occur worldwide and might be more likely when animals fail to detect trains early enough to perform effective escape behaviour. Detection could be especially limited where tracks curve around hills, reducing visibility and audibility of approaching trains, but no literature has examined this potential in the context of terrestrial transportation collisions with wildlife. At 10 locations in a mountain park where the railway curved around elevated topography, we measured train audibility (as a ratio of train to background sound during approach) and developed a physical model to simulate train audibilities along 45.6 km of the same track. We compared both measured and simulated values to locations of wildlife–train collisions over a 35 year period. More wildlife collisions occurred at locations where measured train audibilities (averaged between train directions at each location) were lower and for the lowest quartile of simulated audibilities. Hill height appeared to reduce train audibility for approaches around curves, but track curvature did not predict audibility overall. Background noise from adjacent road traffic reduced train audibility, as did high train speeds and down-grade travel. Our results suggest that co-occurrence of lower audibility with other risk factors will make it difficult to predict and mitigate collision risk from audibility alone.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".