An assessment of moose and elk train collisions in Ontario, Canada.
Bibliographic record
Abstract
To better understand train collision mortality of moose (Alces alces) and elk (Cervus elaphus) in Ontario, we measured collisions along a 20 km segment of railway using post-winter railbed surveys (11 consecutive years), remote cameras, and radio-telemetered elk. We used these data to estimate provincial moose-train collision rates by extrapolating collision rates, moose density, and amount of high use railway per Wildlife Management Unit (WMU). The annual collision rate varied from 0 to 7 moose and 2 to 22 elk on the 20 km section of railway; the combined collision rate of moose and elk was highest in winters with above average snowfall. The extrapolated collision rates of moose indicated that ~1/3 of WMUs had a rate > 0.08 moose/km high use railway/yr; ~2/3 had a rate > 0.04. A conservative estimate of annual mortality was ~265 moose province-wide. Given that railway expansion is predicted globally, and specifically in Ontario, planning should include potential mitigation strategies that minimize ungulate-train collisions.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".