Evolution of Covariance in the Mammalian Skull
Bibliographic record
Abstract
The skull is a developmentally complex and highly integrated structure. Integration, which is manifested as covariance among structures, enables the skull and associated soft tissues to maintain function both across ontogeny within individuals and across the ranges of size and shape variation among individuals. Integration also contributes to evolvability by structuring the phenotypic expression of genetic variation. We argue that the pattern of covariation seen in complex phenotypes such as the skull results from the overlaying of variation introduced by developmental and environmental factors at different stages of development. Much like a palimpsest, the covariation structure of an adult skull represents the summed imprint of a succession of effects, each of which leaves a distinctive covariation signal determined by the specific set of developmental interactions involved. Covariance evolves either by altering the variance of one of these sequential effects or through the introduction of a novel covariance producing effect. Either way is consistent with the notion that evolutionary change occurs through tinkering. We illustrate these principles through analyses of how genetic perturbations acting at different developmental stages (embryonic, fetal, and postnatal) influence the covariance structure of adult mouse skulls. As predicted by the model, the results illustrate the intimate relationship between the modulation of variance and the expression of covariance. The results also demonstrate that covariance patterns have a complex relationship to the underlying developmental architecture, thus highlighting problems with making inferences about developmental relationships (e.g. modularity) based on covariation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".