Craniofacial variability and modularity in macaques and mice
Bibliographic record
Abstract
Evolutionary developmental biology of primates will be driven largely by the developmental biology of the house mouse. Inferences from how known developmental perturbations produce phenotypic effects in model organisms, such as mice, to how the same perturbations would affect craniofacial form in primates must be informed by comparisons of phenotypic variation and variability in mice and the primate species of interest. We use morphometric methods to compare patterns of cranial variability in homologous datasets obtained for two strains of laboratory mice and rhesus macaques. C57BL/6J represents a common genetic background for transgenic models. A/WySnJ mice exhibit altered facial morphology which results from reduction in the growth of the maxillary process during formation of the face. This is relevant to evolutionary changes in facial prognathism in nonhuman primate and human evolution. Rhesus macaques represent a nonhuman primate about which a great deal of phenotypic and genetic information is available. We find significant similarities in covariation patterns between the C57BL/6J mice and macaques. Among-trait variation in genetic and phenotypic variances are fairly concordant among the three groups, but among-trait variation in developmental stability is not. Finally, analysis of modularity based on phenotypic and genetic correlations did not reveal a consistent pattern in the three groups. We discuss the implications of these results for the study of evolutionary developmental biology of primates and outline a research strategy for integrating mouse genomics and developmental biology into this emerging field.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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 | 0.000 |
| 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".