Unraveling the complexity of the Skull: An evo‐devo approach
Bibliographic record
Abstract
The complexity of the vertebrate skull has intrigued scientists for decades. This complexity largely exists because the bones and cartilages that make up the skull have different embryonic origins, develop at different times and ossify via different mechanisms. Understanding both the evolutionary history of skeletal elements and their developmental pathways can provide novel insight into this complexity. I will discuss our work on the skeletal elements associated with the eye, specifically the sclerotic ring. The sclerotic ring is composed of neural crest derived intramembranous bones and is situated in the eyeball (sclera) of many vertebrates, including reptiles and bony fish. Our comparative approach in different organisms and our phylogenetic analyses has enabled us to understand the diversity of this part of the skull. Manipulating the embryo results in multiple effects and provides important clues to underlying the mechanisms governing craniofacial plasticity and constraint at the cellular and tissue level. Since evolutionary and developmental trajectories operate at very different temporal scales, understanding both will provide a more comprehensive view of the vertebrate skull and its complexities.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".