Ontogenetic Color Switching in Lizards as a by-Product of Guanine Cell Development
Bibliographic record
Abstract
Abstract Many animals undergo dramatic changes in colour during development 1,2 . Changes in predation risk during ontogeny are associated with spectacular switches in defensive colours, typically involving the replacement of skin or the production of new pigment cells 3 . Ontogenetic colour systems are ideal models for understanding the evolution and formation mechanisms of animal colour which remain largely enigmatic 2 . We show that defensive colour switching in lizards arises by reorganization of a single photonic system, as an incidental by-product of chromatophore maturation. The defensive blue tail colour of hatchling A. beershebensis lizards is produced by light scattering from premature guanine crystals in underdeveloped iridophore cells. Camouflaged adult tail colours emerge upon reorganization of the guanine crystals into a photonic reflector during chromatophore maturation. The substituent guanine crystals form by the attachment of individual nanoscopic plates, which coalesce during growth to form single crystals. Our results show that the blue colour of hatchlings is a fortuitous, but necessary, precursor to the development of adult colour. Striking functional colours in animals can thus arise not as distinct evolutionary innovations but via exploitation of the timing of naturally occurring changes in chromatophore cell development.
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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 | 0.001 | 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".