Effects of developmental and functional interactions on mouse cranial variability through late ontogeny
Bibliographic record
Abstract
The mammalian skull performs a variety of functions and its growth and development mirrors this complexity. Cranial growth and development have been actively studied for many years. Despite this interest, the variation in the patterns and processes of skull growth has attracted little attention. An important and unanswered question is the extent to which patterns of cranial covariation and variation are dynamically reworked throughout postnatal growth. To address this question, we examine patterns of variability in random-bred mouse skulls aged 35, 90, and 150 days. Using a battery of both Procrustes coordinate and Euclidean distance-based methods, we measure mean shape, canalization, developmental stability, and morphological integration in these skulls. We predict that the patterns of variability are dynamic, particularly between the youngest and the two oldest age groups due to the influence of functional effects such as postweaning mastication. We also hypothesize that patterns of variability are structured by the same functional and developmental factors that have been shown to influence cranial growth in primates. Our results indicate that contrary to our predictions, patterns of canalization, developmental stability, and morphological integration are stabilized before 35 days. The mean shape, however, changed significantly with growth. We found that only the facial region showed significant integration as predicted by the functional matrix model used in other studies of integration. These results indicate that phenotypic integration in these mice does not closely match those found for primate species, suggesting that comparisons between species should be made with care.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".