The Phenogenomics of Craniofacial Shape
Bibliographic record
Abstract
Developmental biology has only recently begun to focus on the mechanisms that generate variation within species. Addressing this difficult question can inform our understanding of dysmorphology. It is also central to the developmental basis for evolvability. Here we review our work on the developmental determinants of shape and size variation in the vertebrate craniofacial complex. We present analyses of the Collaborative Cross founder strains and crosses, mouse mutants and human cranial and facial morphometric data to show that variation in the mammalian skull tends to be structured along axes of covariation. Such axes relate to variation in key developmental processes such as chondrocranial or brain growth, the outgrowth of the facial prominences, and the allometric effects of overall cranial growth. Identifying such key developmental processes is an important first step towards unraveling the complex developmental‐genetic determinants of phenotypic variation. Proceeding beyond this point requires complementary strategies. One is the development of methods for quantitative integration across levels of the genotype‐phenotype map. Another is the use of predictive simulation of complex developmental processes. We present our progress towards these goals and how these strategies can inform our understanding of the developmental basis for phenotypic variation in the vertebrate craniofacial complex.
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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 | 0.001 | 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 | 0.001 |
| 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".