Comparative Morphology of the Avian Cerebellum: I. Degree of Foliation
Bibliographic record
Abstract
Despite the conservative circuitry of the cerebellum, there is considerable variation in the shape of the cerebellum among vertebrates. One aspect of cerebellar morphology that is of particular interest is the degree of folding, or foliation, of the cerebellum and its functional significance. Here, we present the first comprehensive analysis of variation in cerebellar foliation in birds with the aim of determining the effects that allometry, phylogeny and development have on species differences in the degree of cerebellar foliation. Using both conventional and phylogenetically based statistics, we assess the effects of these variables on cerebellar foliation among 91 species of birds. Overall, our results indicate that allometry exerts the strongest effect and accounts for more than half of the interspecific variation in cerebellar foliation. In addition, we detected a significant phylogenetic effect. A comparison among orders revealed that several groups, corvids, parrots and seabirds, have significantly more foliated cerebella than other groups, after accounting for allometric effects. Lastly, developmental mode was weakly correlated with relative cerebellar foliation, but incubation period and fledging age were not. From our analyses, we conclude that allometric and phylogenetic effects exert the strongest effects and developmental mode a weak effect on avian cerebellar foliation. The phylogenetic distribution of highly foliated cerebella also suggests that cognitive and/or behavioral differences play a role in the evolution of the cerebellum.
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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 | 0.002 | 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".