Integration and the Developmental Genetics of Allometry
Bibliographic record
Abstract
Allometry refers to the ways in which organismal shape is associated with size. It is a special case of integration, or the tendency for traits to covary, in that variation in size is ubiquitous and evolutionarily important. Allometric variation is so commonly observed that it is routinely removed from morphometric analyses or invoked as an explanation for evolutionary change. In this case, familiarity is mistaken for understanding because rarely do we know the mechanisms by which shape correlates with size or understand their significance. As with other forms of integration, allometric variation is generated by variation in developmental processes that affect multiple traits, resulting in patterns of covariation. Given this perspective, we can dissect the genetic and developmental determinants of allometric variation. Our work on the developmental and genetic basis for allometric variation in craniofacial shape in mice and humans has revealed that allometric variation is highly polygenic. Different measures of size are associated with distinct but overlapping patterns of allometric variation. These patterns converge in part on a common genetic basis. Finally, environmental modulation of size often generates variation along allometric trajectories, but the timing of genetic and environmental perturbations can produce deviations from allometric patterns when traits are differentially sensitive over developmental time. These results question the validity of viewing allometry as a singular phenomenon distinct from morphological integration more generally.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".