Factors driving sexual dimorphism and colour variability in the Achala Copper Lizard (<i>Pristidactylus achalensis</i>), an endemic species to the highland mountains in central Argentina
Bibliographic record
Abstract
The intensity of mating competition varies according to the temporal and spatial distribution of individuals. Measuring sexual dimorphism over time and interpreting the association between individuals is therefore important if we aim to understand how sexual traits are influenced. We examined sex differences in the Achala Copper Lizard (Pristidactylus achalensis (Gallardo, 1964)), an endemic species from the highest part of mountains of central Argentina. Over 4 years, we explored sex-specific variation in body size, head size, interlimb length, and body colouration. Furthermore, we evaluated how these traits varied temporally, and we also explored whether the spatial distribution of individuals is explained by variation in these traits. We found that P. achalensis is a species with sexual dimorphism in multiple characters, including body size, head size, and colouration. Interestingly, some traits related to mating, such as head width, show a temporal variability in both sexes, whereas other traits, such as colouration, varies seasonally only in males. Our results underline the intriguing possibility of seasonal morphological changes related to mating, and more broadly that sex differences are influenced by sexual selection pressures mediated by temporal variation in mate competition.
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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 | 0.000 |
| 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".