Generally specialized or especially general? Habitat selection by Snapping Turtles (<i>Chelydra serpentina</i>) in central Ontario
Bibliographic record
Abstract
Habitat selection is the disproportionate use of habitat compared with availability. Many studies have focused on specialists, but few have considered habitat selection in populations that are generalists, which can be composed of generalist individuals or individuals that specialize on different habitats. We tested habitat selection and individual specialization in a northern population of a supposed generalist, the Snapping Turtle ( Chelydra serpentina (L., 1758)), during the active season and winter using telemetry. Habitat selection was tested at two spatial scales by comparing random points to home ranges and turtle locations using Euclidean distances. Turtles selected home ranges from the habitats available in the population range. However, at the population level, all aquatic habitats were equally preferred, and the population behaved as a generalist owing to individuals specialized on different habitats. Over half of the individuals showed evidence of individual specialization on different habitat types. Turtles did not select habitat within home ranges during the active season, but overwintering turtles chose locations that were colder than haphazard stations in the same habitats, likely to reduce metabolic costs and the risk of acidosis. These findings have implications for the management of this species at risk and for understanding the evolution of resource generalization.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".