Thermoregulation as a function of thermal quality in a northern population of painted turtles, Chrysemys picta
Bibliographic record
Abstract
Body temperature affects nearly all processes of ectotherms. Reptiles do not generate sufficient body heat to regulate their body temperature internally and therefore use behavioural thermoregulation. We determined whether thermoregulatory effort varied among seasons in an environment where large temporal differences in environmental temperatures (Te) exist. We took 31 297 internal body temperature (Tb) measurements from 18 painted turtles ( Chrysemys picta (Schneider, 1783)) throughout their active season. We estimated Te with physical models and water temperatures. We measured the range of preferred body temperatures (Tset) in a thermal gradient. Tset was 21.3–25.0 °C. We used Tb, Te, and Tset to calculate standard thermoregulation indices (Ex and de – db). An Ex of 40.7% and a de – db of 2.4 °C indicated that painted turtles are moderate thermoregulators, despite inhabiting a high-cost environment. Effort to regulate Tb increased as the thermal quality of the habitat decreased. Thermoregulatory effort was higher when Tset could not be achieved. Painted turtles put more effort in thermoregulation in the early season than in the rest of the season. This within-species pattern follows the pattern seen among species. This study is the first to measure Tb internally and to apply standard thermoregulation indices to free-ranging turtles.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".