Distress Calls of Birds in a Neotropical Cloud Forest<sup>1</sup>
Bibliographic record
Abstract
ABSTRACT Distress calls are loud, harsh calls given by some species of birds when they are captured by a predator or handled by humans. We recorded the frequency of distress calls and struggling behavior in 40 species of birds captured in mist nets during the dry season in a Costa Rica cloud forest. We tested the following hypotheses proposed to explain the function of distress calls: (1) calling for help from kin or reciprocal altruists; (2) warning kin; (3) eliciting mobbing behavior; (4) startling the predator; and (5) distracting the predator through attraction of additional predators. Our results did not support the calling‐for‐help, warning kin, or mobbing hypotheses. Indeed, genera that regularly occurred with kin or in flocks were not more likely to call than non‐flocking genera. There was no relationship between calling frequency and struggling behavior as predicted by the predator startle hypothesis. Genera of larger birds tended to call more than smaller birds, providing some support for both the predator distraction hypothesis and predator startle hypotheses. Calls of higher amplitude may be more effective in startling the predator. Distress calls of larger birds may also travel greater distances than those of smaller birds, supporting the predator manipulation hypothesis, but this requires further testing.
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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 | 0.000 | 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".