Thermoregulation and habitat selection in wood turtles <i>Glyptemys insculpta</i>: chasing the sun slowly
Bibliographic record
Abstract
1. It is widely accepted that reptiles are able to regulate behaviourally their body temperature (T(b)), but this generalization is primarily based on studies of lizards and snakes in the temperate zone. Because the precision of T(b) regulation may vary considerably between taxa and over geographical ranges, studies of semi-terrestrial turtles in climatic extremes are relevant to the understanding of reptilian thermoregulation. 2. We studied thermoregulation in 21 free-ranging wood turtles (Glyptemys insculpta) at the northern limit of their range in Québec, using miniature data loggers to measure their internal T(b) and external temperature (T(ext)) continuously. We simultaneously recorded the available operative environmental temperature (T(e)) using 23 physical models randomly moved within each habitat type, and we located turtles using radiotelemetry. 3. The habitat used by wood turtles was thermally constraining and the target temperature (T(set)) was only achievable by basking during a short 5-h time window on sunny days. Wood turtles did show thermoregulatory abilities, as determined by the difference between turtle T(b) distribution and the null distribution of T(e) that resulted in T(b) closer to T(set). Although most individuals regulated their T(b) between 09.00 h and 16.00 h on sunny days, regulation was imprecise, as indicated by an index of thermoregulation precision (| T(b) - T(set) |). 4. The comparison of habitat use to availability indicated selection of open habitats. The hourly mean shuttling index (| T(ext) - T(b) |) suggested that turtles used sun/shade shuttling from 09.00 to 16.00 h to elevate their T(b) above mean T(e). 5. Based on laboratory respirometry data, turtles increased their metabolic rate by 20-26% over thermoconformity, and thus likely increased their energy gain which is assumed to be constrained by processing rate at climatic extremes.
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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) | 0.001 | 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".