Morphology and evolution of the snake cornea
Bibliographic record
Abstract
To investigate whether the thickness of the cornea in snakes correlates with overall anatomy, habitat or daily activity pattern, we measured corneal thickness using optical coherence tomography scanning in 44 species from 14 families (214 specimens) in the collection at the Natural History Museum (Denmark). Specifically, we analyzed whether the thickness of the cornea varies among species in absolute terms and relative to morphometrics, such as body length, spectacle diameter, and spectacle thickness. Furthermore, we examined whether corneal thickness reflects adaptation to different habitats and/or daily activity patterns. The snakes were defined as arboreal (n = 8), terrestrial (n = 22), fossorial (n = 7), and aquatic (n = 7); 14 species were classified as diurnal and 30 as nocturnal. We reveal that the interspecific variation in corneal thickness is largely explained by differences in body size, but find a tendency towards thicker corneas in diurnal (313 ± 227 μm) compared to nocturnal species (205 ± 169 μm). Furthermore, arboreal snakes had the thickest corneas and fossorial snakes the thinnest. Our study shows that body length, habitat, and daily activity pattern could explain the interspecific variation in corneal morphology among snakes. This study provides a quantitative analysis of the evolution of the corneal morphology in snakes, and it presents baseline values of corneal thickness of multiple snake species. We speculate that the cornea likely plays a role in snake vision, despite the fact that results from previous studies suggest that the cornea in snakes is not relevant for vision (Sivak, Vision Research, 1977, 17, 293-298).
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".