Eastern Hog‐Nosed Snake Habitat Selection at Multiple Spatial Scales in Ontario, Canada
Bibliographic record
Abstract
ABSTRACT Habitat loss is the greatest contributor to the decline of species globally. To prioritize protection of imperiled species, it is important to examine habitat use at multiple spatial scales because the availability of different resources and habitat features is scale dependent. We conducted a radio‐telemetry study in the Long Point region of Ontario, Canada, in 2009 and 2010 to examine habitat selection at multiple spatial scales by eastern hog‐nosed snakes (Heterodon platirhinos), a species at risk in Canada. We documented the habitat composition of home ranges compared to the surrounding landscape, the selection of locations within home ranges based on classified satellite imagery, and the use of microhabitat features based on site characterization in the field. At the scale of the home ranges, hog‐nosed snakes avoided areas of agriculture and selected sand barrens. Within home ranges, hog‐nosed snakes selectively used areas altered by humans (e.g., residential sites, openings in tree plantations). Microhabitats used by hog‐nosed snakes had more woody debris, logs, and lower vegetative coverage than adjoining random sites. Because hog‐nosed snakes prefer open areas and require sandy soils for nesting, management efforts should focus on the conservation and maintenance of sand barrens and patches of early successional forest. © 2021 The Wildlife Society.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".