Thermoregulatory behaviour of gravid and non‐gravid female grass snakes (<i>Natrix natrix</i>) in a thermally limiting high‐latitude environment
Bibliographic record
Abstract
Abstract All else being equal, ectotherms should maintain body temperatures (Tbs) favourable for temperature‐sensitive biological functions, such as digestion and locomotion. Physical environments in the temperate zone are often thermally variable, however, thus making it difficult to maintain optimum Tbs. Radiotelemetry and a semi‐natural enclosure were used to monitor Tbs of grass snakes Natrix natrix at Canterbury, Kent, U.K. Operative temperatures (Tes) were measured using snake models (copper‐pipe models) placed in a variety of microhabitats to determine availability of thermoregulatory opportunities. A modification of Huey & Slatkin's (1976) regression model, piecewise regression, was used to evaluate thermoregulatory behaviour. Grass snakes could achieve high mean Tbs (> 30°C) most often during midday (08:00–20:00), but only for 55–61% of the study period. Overall, non‐gravid snakes maintained higher and less variable mean daytime Tbs than gravid snakes. From piecewise regression, it was determined that grass snakes initiated thermoregulation at a Te of 38.44 °C, corresponding to a Tb of 27.7 °C. Three main conclusions can be drawn from this study: (1) thermoregulatory opportunities for grass snakes were limited; (2) nonetheless, when conditions were sufficiently warm, there was clear evidence that grass snakes could thermoregulate; (3) contrary to expectation, gravid females actually maintained lower and more variable Tbs than non‐gravid females. The consequences of these thermoregulatory patterns for the fitness of snakes have yet to be determined.
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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".