Endocast structures are reliable proxies for the sizes of corresponding regions of the brain in extant birds
Bibliographic record
Abstract
Endocasts are increasingly relied upon to examine avian brain evolution because they can be used across extant and extinct species. The endocasts of birds appear to be relatively faithful representatives of the external morphology of their brains, but it is unclear how well the size of a surface feature visible on endocasts reflects the volume of the underlying brain region. The optic lobe and the Wulst are two endocast structures that are clearly visible on the external surface of avian endocasts. As they overlie two major visual regions of the brain, the optic tectum and hyperpallium, the surface areas of the optic lobe and Wulst, respectively, are often used to infer visual abilities. To determine whether the surface area of these features reflects the volume of the underlying brain regions, we compared the surface areas of the optic lobes and Wulsts from digital endocasts with the volumes of the optic tecta and hyperpallia from the literature or measured from histological series of brains of the same species. Regression analyses revealed strong, statistically significant correlations between the volumes of the brain regions and the surface areas of the overlying endocast structures. In other words, the size of the hyperpallium and optic tectum can be reliably inferred from the surface areas of the Wulst and optic lobe, respectively. This validation opens the possibility of estimating brain-region volumes for extinct species in order to gain better insights in their visual ecology. It also emphasizes the importance of adopting a quantitative approach to the analysis of endocasts in the study of brain evolution.
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 | 0.000 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".