Fight song: variation in singing behaviour and song structure during natural agonistic interactions in a tropical songbird, Adelaide's Warbler ( <i>Setophaga adelaidae</i> )
Bibliographic record
Abstract
Birds may use their singing behaviours and song structure as agonistic signals in territorial encounters. We conducted an observational study to test this hypothesis in male Adelaide's Warblers Setophaga adelaidae , a tropical songbird that defends a territory year‐round. We described two singing behaviours and nine song structure variables (including vocal performance measures) around the time of natural territorial encounters. We found that birds decreased their song rate and song type switching around the time of encounters. Our findings allow us to reject the hypotheses that male Adelaide's Warblers use high song type diversity or high song rates as agonistic signals. They are, however, consistent with the hypothesis that repetitive singing may be an agonistic signal. Our results also suggest that song may not be an important agonistic signal in close range encounters and low song rates may provoke aggression. This study demonstrates how an observational approach grounds our understanding of aggressive signalling in the reality of natural agonistic encounters. Interestingly, our findings suggest that male Adelaide's Warblers mediate aggressive encounters with repetitive songs rather than high vocal performance or song diversity.
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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".