Regulation of Exposure to Ultraviolet Light in Bearded Dragons (Pogona vitticeps) in Relation to Temperature and Scalation Phenotype
Bibliographic record
Abstract
Exposure to ultraviolet (UV) light has both physiological benefits as well as costs. Many lepidosaur reptiles can behaviorally self-regulate their exposure to UV light in order to take advantage of the benefits of UV light while minimizing the costs. Furthermore, lepidosaur scales have been conceptualized by some as a barrier to the penetration of UV light. Here we examine regulation of self-exposure to UV light in three different phenotypes of Bearded Dragon (Pogona vitticeps): wild type, animals exhibiting scales of reduced prominence (‘Leatherback'), and scaleless animals (‘Silkback'). Silkbacks on average chose to expose themselves to lower levels of UV light irradiation than Leatherbacks or wild types did. Bearded Dragons of all scalation phenotypes on average received higher UV irradiation when they were in the cold section of a UV gradient apparatus compared to when they were in the hot section of the apparatus. This either demonstrates that Bearded Dragons under higher UV irradiances choose cooler temperatures or demonstrates that Bearded Dragons at cooler temperatures choose higher UV irradiances. The relationship between chosen temperature and chosen UV light irradiance was not affected by scalation phenotype. This study highlights external influences on the mechanism that regulates UV self-exposure behavior in lepidosaur reptiles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".