Genetic and environmental drivers of colour and pattern in the Australian jacky dragon ( <i>Amphibolurus muricatus</i> )
Bibliographic record
Abstract
The underlying drivers of variation in the colouration (colour and pattern) of animals can be genetic, non-genetic, or more likely, a combination of both. Understanding the role of heritable genetic elements, as well as non-genetic factors such as age, habitat or temperature, in shaping colouration can provide insight into the evolution and function of these traits, as well as the speed of response to changing environments. This project examined the genetic and non-genetic drivers of continuous variation in colouration in a lizard, the jacky dragon (Amphibolurus muricatus). We leveraged a large captive experiment that manipulated parental and offspring thermal environment to simultaneously estimate the genetic and non-genetic drivers of variation in colouration. We found that the overall brightness, the elongation of the longitudinal stripes on the dorsum and the contrast between light and dark patches of the pattern were all heritable. Colouration varied according to the age of the hatchling; however, the thermal environment of neither the parents nor offspring contributed significantly to colouration. It appears that developmental plasticity and maternal effects associated with temperature are not important drivers of variation in our measures of colouration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".