Quantitative analyses link modulation of sonic hedgehog signaling to continuous variation in facial growth and shape
Bibliographic record
Abstract
Variation is an intrinsic feature of biological systems, yet developmental biology does not frequently address population-level phenomena. Sonic hedgehog (SHH) signaling activity in the vertebrate forebrain and face is thought to contribute to continuous variation in the morphology of the upper jaw, but despite its potential explanatory power, this idea has never been quantitatively assessed. Here, we test this hypothesis with an experimental design that is explicitly focused on the generation and measurement of variation in multivariate shape, tissue growth, cellular behavior and gene expression. We show that the majority of upper jaw shape variation can be explained by progressive changes in the spatial organization and mitotic activity of midfacial growth zones controlled by SHH signaling. In addition, nonlinearity between our treatment doses and phenotypic outcomes suggests that threshold effects in SHH signaling may play a role in variability in midfacial malformations such as holoprosencephaly (HPE). Together, these results provide novel insight into the generation of facial morphology, and demonstrate the value of quantifying variation for our understanding of development and disease.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".