The Effect of Thermal Quality on the Thermoregulatory Behavior of the Bearded Dragon<i>Pogona vitticeps</i>: Influences of Methodological Assessment
Bibliographic record
Abstract
Metabolic functions are generally optimized within a narrow range of body temperatures (T(b)'s), conferring thermoregulation great importance to the survival and fitness of an animal. In lizards, T(b) regulation is mainly behavioral, and the metabolic costs associated with behavioral thermoregulation are primarily locomotory. In reptiles, however, it has been proposed that they thermoregulate less precisely when the associated costs, metabolic or otherwise, are high. Such a strategy enhances fitness by allowing lizards to be more flexible to changing environmental conditions while maximizing the benefits of maintaining a high T(b) and minimizing energy expenditure. We evaluated the behavioral thermoregulation of inland bearded dragons Pogona vitticeps under various thermal quality conditions requiring different locomotory investment for thermoregulation. The selected ambient temperature and preferred T(b) ranges increased at lower environmental thermal qualities, indicating a decrease in thermoregulatory precision in environments where the costs associated with thermoregulation were high. The level of thermoregulation was also affected, exhibiting a decrease in preferred T(b) of approximately 2 degrees C at the lowest-thermal-quality treatment. These data provide important implications for the procedural assessment of preferred T(b) and a better understanding of thermal set points in reptiles in general. Our results emphasize that the precise maintenance and assessment of preferred T(b) is contingent on the quality of the environment, laboratory or natural, that the animal inhabits.
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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".