An analysis of avian vocal performance at the note and song levels
Bibliographic record
Abstract
Abstract Sexual displays that require extreme feats of physiological performance have the potential to reliably indicate the signaller’s skill. The hypothesis that the structure of bird song is physiologically constrained remains controversial. We tested for evidence of performance constraints in Adelaide’s warblers ( Setophaga adelaidae ) songs. At the note level, we identified three trade-offs with well-defined limits. At the song level, we identified two trade-offs, but their limits were less well-defined than the note-level limits. Trade-offs at both levels suggest that song structure is constrained by limits to the speed of both frequency modulation (while vocalizing and between notes) and respiration. Individual males experience the same trade-offs that characterize the population, but the intensity of those trade-offs varies among individuals. Performance metrics derived from the observed limits to performance varied moderately among individuals and strongly among song types. Note-level performance metrics were positively skewed, as predicted by the hypothesis that this population has experienced positive selection for constrained performance. We conclude that physiological limits on frequency modulation and respiration constrain song structure in male Adelaide’s warblers. Further work is needed to determine whether receivers respond to natural levels of variation in performance, and whether performance correlates with singer quality.
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 | 0.001 |
| 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".