Intraspecific and interspecific variation in use of forest-edge habitat by snakes
Bibliographic record
Abstract
Variation in use of edge habitat among populations and species of snakes should reflect underlying causes (e.g., thermal ecology, prey availability) and consequences (e.g., predation on birds' nests) of habitat selection. We compared the habitat use of ratsnakes, Elaphe obsoleta (Say in James, 1823), in Illinois and Ontario and compared habitat use by ratsnakes and racers, Coluber constrictor (L., 1758), in Illinois. Ratsnakes in Illinois used upland forest more and forest edges less than ratsnakes in Ontario. Female ratsnakes in Illinois used edges less than males, regardless of their reproductive status. Relative to ratsnakes, racers preferred forest edges and avoided forest interior. Female racers used edges more than males, especially while gravid. These results, and most of the seasonal patterns in habitat use, were broadly consistent with variation expected from differences in thermoregulatory needs, although other factors potentially influencing habitat use cannot be ruled out. Although it has been proposed that some forest fragmentation is likely to be beneficial for ratsnakes in Canada, such fragmentation may be detrimental to ratsnakes in Illinois but beneficial to racers. Thus, relative to forest-interior species, edge-nesting birds should be more vulnerable to predation by ratsnakes in Ontario, and fragmentation should increase the vulnerability of forest birds to nest predation by racers.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".