Coloration is highly variable but is not always an honest signal in the painted turtle, <i>Chrysemys Picta</i>
Bibliographic record
Abstract
Color signals in animals are often honest, containing information about the individual for potential mates or predators. Both males and females may have honest color signals, though female signals are less often assessed for honesty. Different colorimetric traits, such as overall brightness, hue, ultraviolet brightness, or carotenoid chroma, may be assessed by receivers for information. Painted Turtles, Chrysemys picta (Schneider, 1783), are a brightly colored and widely distributed freshwater turtle species with no visible sexual dichromatism, but the function of their coloration is unknown. We assess two populations of Painted Turtles to compare colorimetric traits across and within populations, and to determine whether any color traits correlate with innate immune function. We find that there is greater carotenoid chroma on areas of the shell not typically associated with courtship than on the neck stripes that are thought to have a role in mate choice. We also find that only one measure of coloration in one color patch, the carotenoid chroma of the neck stripes, is correlated with bactericidal capacity, and in only one population. This system provides an example of a common species having vibrant but understudied coloration that may provide insights into unknown functions of color or uncommon sexual selection variation.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".