Adaptation in brain structure and respiratory and olfactory structures across environmental gradients in African and North American muroid rodents
Bibliographic record
Abstract
Morphometric studies of 3D micro CT-scanned images can provide insights into the evolution of the brain and sensory structures but such data are still scarce for the most diverse mammalian order of rodents. From reviewed and new data, we tested for convergence to extreme aridity and high elevation in the sensory and brain morphology of rodents, from morphometric data from micro-CT X-ray scans of 174 crania of 16 species of three distantly related African murid (soft-furred mice, Praomyini, laminate-toothed rats, Otomyini, and gerbils, Gerbillinae) clades and one North American cricetid (deer mice and white-footed mice, Peromyscus) clade. Recent studies demonstrated convergent evolution acting on the oval window area of the cochlea (enlarged in extremely arid-adapted species of Otomyini and Gerbillinae) and on endocranial volume (reduced in high elevation taxa of Otomyini and Peromyscus). However, contrary to our predictions, we did not find evidence of convergence in brain structure to aridity, or in the olfactory/respiratory system (turbinate bones) to high elevation. Brain structure differed, particularly in the petrosal lobules of the cerebellum and the olfactory bulbs, between Otomyini and Gerbillinae, with extreme arid-adapted species in each clade being highly divergent (not convergent) from other species in the same clade. We observed greater "packing" of the maxillary turbinate bones, which have important respiratory functions, in Peromyscus mice from high and low elevations compared to the high-elevation African Praomyini, but more complex patterns within Peromyscus, probably related to trade-offs in respiratory physiology and heat exchange in the nasal epithelium associated with high-elevation adaptation.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".