Morphological diversity in the orbital bones of two teleosts with experimental and natural variation in eye size
Bibliographic record
Abstract
Background: Understanding differences in tissue morphology has not been well researched, yet provides crucial insight into evolution. We investigate the effect of eye reduction on the shape of surrounding bones by examining two morphs of the Mexican tetra (Tinaja cavefish and sighted fish), F1 intermediates, zebrafish, a sighted tetra after lens removal and a zebrafish mutant, bum‐/‐, which has a degenerating lens. Results: Significantly, by comparing the skulls, we show that there are broadly similar effects on bone shape after eye reduction with bones posterior and dorsal to the eye consistently most affected in both species. We conclude that there are conserved mechanisms underlying bone shape changes in response to a reduced or lost eye. Of interest, when we compare the shapes of individual bones and the mode of eye reduction, differences suggest that the finer details of these underlying mechanisms may indeed vary. We also show that cavefish occupy a unique morphospace with respect to skull morphology and that F1 intermediates are most similar to sighted fish than their cavefish parent. Conclusions: This study highlights the dynamic nature of the vertebrate skull and its ability to respond to tissue changes within the head, a topic which has been largely overlooked in the literature. Developmental Dynamics 244:1109–1120, 2015. © 2015 Wiley Periodicals, Inc.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".