Spatial packing, cranial base angulation, and craniofacial shape variation in the mammalian skull: testing a new model using mice
Bibliographic record
Abstract
The hypothesis that variation in craniofacial shape within and among species is influenced by spatial packing has a long history in comparative anatomy, particularly in terms of primates. This study develops and tests three alternative models of spatial packing to address how and to what extent the cranial base angle is influenced by variation in brain and facial size. The models are tested using mouse strains with different mutations affecting craniofacial growth. Although mice have distinctive crania with small brains, long faces, and retroflexed cranial bases, the results of the study indicate that the mouse cranial base flexes to accommodate larger brain size relative to cranial base length. In addition, the mouse cranial base also extends, but to a lesser degree, to accommodate larger face size relative to cranial base length. In addition, interactions between brain size, face size, and the widths and lengths of the components of the cranial base account for a large percentage of variation in cranial base angle. The results illustrate the degree to which the cranial base is centrally embedded within the covariation structure of the craniofacial complex as a whole.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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 | 0.000 |
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".