Enabling safe passage: predicting river crossing hotspots for a threatened boreal ungulate susceptible to drowning
Bibliographic record
Abstract
Abstract Many mammals cross large rivers to access seasonal habitats, yet river crossing behavior is poorly acknowledged or understood. Crossing large rivers is inherently risky, with vessel traffic and flooding events increasing the risk of drowning. We investigated river crossing behavior by threatened bison (Bison bison) in the boreal forest, using data from 11 GPS-collared animals to identify when and where they cross a major river. We used kernel densities of GPS locations to identify crossing hotspots, and developed resource selection function (RSF) models that used habitat characteristics to explain crossing behavior based on a priori hypotheses. We then predicted high-probability crossing areas along a 400-km stretch of river using the best-supported RSF model. Bison regularly crossed the river (x¯ = 8.6 crossings/100 days), particularly during summer months. Model selection results suggested that bison may have chosen river crossing areas that maximized access to forage. Bison preferred to cross the river near islands and areas with high seismic line densities, both of which were likely preferred summer foraging habitats. In addition, islands may have been used as temporary stopovers while crossing the river, or for relief from biting insects. Bison crossed the river where the channel was relatively narrow (x¯ = 400 ± 213 m [SD]) and chose days when river discharge was low, likely to reduce the risk of drowning, exhaustion, or hypothermia. While based on a small sample of collared bison, predictions about high-probability crossing areas may be used to inform mitigation measures aimed at reducing bison drowning caused by vessel traffic, which is expected to increase as resource development expands in the region. Our approach may be informative for identifying river crossing hotspots for other mammals that cross major rivers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".