Railway mortality for several mammal species increases with train speed, proximity to water, and track curvature
Bibliographic record
Abstract
Railways are a major source of direct mortality for many populations of large mammals, but they have been less studied or mitigated than roads. We evaluated temporal and spatial factors affecting mortality risk using 646 railway mortality incidents for 11 mammal species collected over 24 years throughout Banff and Yoho National Parks, Canada. We divided species into three guilds (bears, other carnivores, and ungulates), compared site attributes of topography, land cover, and train operation between mortality and paired random locations at four spatial scales, and described temporal patterns or mortality. Mortality risk increased across multiple guilds and spatial scales with maximum train speed and higher track curvature, both suggesting problems with train detection, and in areas with high proximity to and amount of water, both suggesting limitations to animal movement. Mortality risk was also correlated, but more varied among guilds and spatial scales, with shrub cover, topographic complexity, and proximity to sidings and roads. Seasonally, mortality rates were highest in winter for ungulates and other carnivores, and in late spring for bears, respectively. Our results suggest that effective mitigation could address train speed or detectability by wildlife, especially at sites with high track curvature that are near water or attractive habitat.
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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 | 0.001 |
| 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) | 0.001 | 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".