Bibliographic record
Abstract
Multivariate analyses of brain composition in mammals, amphibians and fish have revealed the evolution of 'cerebrotypes' that reflect specific niches and/or clades. Here, we present the first demonstration of similar cerebrotypes in birds. Using principal component analysis and hierarchical clustering methods to analyze a data set of 67 species, we demonstrate that five main cerebrotypes can be recognized. One type is dominated by galliforms and pigeons, among other species, that all share relatively large brainstems, but can be further differentiated by the proportional size of the cerebellum and telencephalic regions. The second cerebrotype contains a range of species that all share relatively large cerebellar and small nidopallial volumes. A third type is composed of two species, the tawny frogmouth (Podargus strigoides) and an owl, both of which share extremely large Wulst volumes. Parrots and passerines, the principal members of the fourth group, possess much larger nidopallial, mesopallial and striatopallidal proportions than the other groups. The fifth cerebrotype contains species such as raptors and waterfowl that are not found at the extremes for any of the brain regions and could therefore be classified as 'generalist' brains. Overall, the clustering of species does not directly reflect the phylogenetic relationships among species, but there is a tendency for species within an order to clump together. There may also be a weak relationship between cerebrotype and developmental differences, but two of the main clusters contained species with both altricial and precocial developmental patterns. As a whole, the groupings do agree with behavioral and ecological similarities among species. Most notably, species that share similarities in locomotor behavior, mode of prey capture or cognitive ability are clustered together. The relationship between cerebrotype and behavior/ecology in birds suggests that future comparative studies of brain-behavior relationships will benefit from adopting a multivariate approach.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 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".