Annual cycle of the black‐capped chickadee: Seasonality of singing rates and vocal‐control brain regions
Bibliographic record
Abstract
Black-capped chickadees have a rich vocal repertoire including learned calls and the learned fee-bee song. However, the neural regions underlying these vocalizations, such as HVC, area X, and RA (robust nucleus of arcopallium), remain understudied. Here, we document seasonal changes in fee-bee song production and show a marked peak in singing rate during March through May. Despite this, we found only minimal seasonal plasticity in vocal control regions of the brain in males. There was no significant effect of time of year on the size of HVC, X, or RA in birds collected in January, April, July, and October. We then pooled birds into two groups, those with large testes (breeding condition) and those with small testes (nonbreeding), regardless of time of year. Breeding birds had slightly larger RA, but not HVC or X, than nonbreeding birds. Breeding birds had slightly larger HVC and RA, but not X, as a proportion of telencephalon volume than did nonbreeding birds. Birds collected in July had heavier brains than birds at other times of year, and had the greatest loss in brain mass during cryoprotection. The absence of any overall seasonal change in the vocal-control regions of chickadees likely results from a combination of individual differences in the timing of breeding phenology and demands on the vocal-control regions to produce learned calls year-round.
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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".