Living amidst a sea of agriculture: predicting the occurrence of Canada lynx within an ecological island
Bibliographic record
Abstract
Carnivore populations are increasingly confined to reserves surrounded by anthropogenic development. The boundaries of ecological islands are risky because of habitat loss and human—carnivore conflict. From snow track survey data collected over a 5‐winter period we developed a population‐level resource selection function for Canada lynx Lynx canadensis in Riding Mountain National Park, Canada. This park has been characterized as an ‘ecological island situated amidst a sea of agricultural land’ and while lynx are protected within the park, they are subject to harvest outside of the park. Winter resource selection of lynx increased with higher elevation and in highly suitable habitat for snowshoe hare Lepus americanus and decreased in habitat with greater proportions of agriculture and grasslands, both of which are common along the edge of the park. Habitat with medium to high relative probabilities of lynx occurrence tended to be distributed in the interior of the park. However, the highest relative probability of lynx occurrence was associated with habitat near the southeast border of the park in close proximity to a human community and a lake where snowmobiling, skiing, and snowshoeing are common recreational pursuits. We attribute this relationship to a 25‐year old fire which presently created successional habitat highly suitable for snowshoe hare and correspondingly for lynx in this area. Our results suggest that Canada lynx occurrence tends to be associated with habitat that is highly suitable for their primary prey even if that habitat is located near to sources of human recreational activity.
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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 | 0.001 | 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".