Operative temperatures of eastern garter snakes ( <i>Thamnophis sirtalis sirtalis</i> ) reveal a Goldilocks effect for habitat use
Bibliographic record
Abstract
Abstract Garter snakes ( Thamnophis spp.) are the most widespread reptiles in North America, although evidence suggests that thermal preference has not diverged much among populations or Thamnophis species. To shed light on how thermal decisions influence local habitat use by the eastern garter snake ( Thamnophis sirtalis sirtalis ), we measured the thermal profiles of three habitats differing in canopy cover: open peat, mixed shrub, and closed forest. We installed biophysical models to record operative temperatures at a fine scale and assess habitat thermal quality. We also used coverboards to survey habitat usage. While the open canopy offered the highest thermal quality, we recorded the greatest number of snakes in the mixed shrub which had a lower thermal quality. Since environmental temperatures regularly exceeded the upper thermal limit of T. s. sirtalis in the open canopy, snakes might favour the use of habitats that minimise the odds of overheating. Therefore, open habitats potentially restrict snakes’ activity window and may not be thermally attractive. Our data show that T. s. sirtalis use habitats that vary in thermal quality, but warmer habitats are not necessarily better. Rather, snakes preferentially seek areas that offer a mix of open and closed canopies to suit their thermoregulatory needs.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".