Epigenetic interactions and the structure of phenotypic variation in the cranium
Bibliographic record
Abstract
Understanding the developmental and genetic basis for evolutionarily significant morphological variation in complex phenotypes such as the mammalian skull is a challenge because of the sheer complexity of the factors involved. We hypothesize that even in this complex system, the expression of phenotypic variation is structured by the interaction of a few key developmental processes. To test this hypothesis, we created a highly variable sample of crania using four mouse mutants and their wild-type controls from similar genetic backgrounds with developmental perturbations to particular cranial regions. Using geometric morphometric methods we compared patterns of size, shape, and integration in the sample within and between the basicranium, neurocranium, and face. The results highlight regular and predictable patterns of covariation among regions of the skull that presumably reflect the epigenetic influences of the genetic perturbations in the sample. Covariation between relative widths of adjoining regions is the most dominant factor, but there are other significant axes of covariation such as the relationship between neurocranial size and basicranial flexion. Although there are other sources of variation related to developmental perturbations not analyzed in this study, the patterns of covariation created by the epigenetic interactions evident in this sample may underlie larger scale evolutionary patterns in mammalian craniofacial form.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".