Selection for Divergent Reproductive Investment Affects Neuron Size and Foliation in the Cerebellum
Bibliographic record
Abstract
The cerebellum has a highly conserved internal circuitry, but varies greatly in size and morphology within and across species. Despite this variation, the underlying volumetric changes among the layers of the cerebellar cortex or their association with Purkinje cell numbers and sizes is poorly understood. Here, we examine intraspecific scaling relationships and variation in the quantitative neuroanatomy of the cerebellum in Japanese quail (Coturnix japonica) selected for high or low reproductive investment. As predicted by the circuitry of the cerebellum, the volumes of the constituent layers of the cerebellar cortex were strongly and positively correlated with one another and with total cerebellar volume. The number of Purkinje cells also significantly and positively co-varied with total cerebellar volume and the molecular layer, but not the granule cell layer or white matter volumes. Purkinje cell size and cerebellar foliation did not significantly covary with any cerebellar measures, but differed significantly between the selection lines. Males and females from the high-investment lines had smaller Purkinje cells than males and females from the low-investment lines and males from the high-investment lines had less folded cerebella than quail from the low-investment lines. These results suggest that within species, the layers of the cerebellum increase in a coordinated fashion, but Purkinje cell size and cerebellar foliation do not increase proportionally with overall cerebellum size. In contrast, selection for differential reproductive investment affects Purkinje cell size and cerebellar foliation, but not other quantitative measures of cerebellar anatomy.
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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 | 0.000 |
| 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 | 0.000 | 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".