Enemy Recognition of Reed Warblers (<i>Acrocephalus scirpaceus</i>): Threats and Reproductive Value Act Independently in Nest Defence Modulation
Bibliographic record
Abstract
Abstract Organisms should respond more aggressively towards species perceived as a danger to their offspring, but intensity of defence may be gauged by the value of current offspring weighed against the value of future reproductive opportunities. We tested whether defensive responses of nesting reed warblers (Acrocephalus scirpaceus) are the result of an interaction effect between the type of stimulus confronted and the value of the warbler’s nesting attempt. We quantified the ability of reed warblers to discriminate among brood parasites, nestling predators and non‐threatening species at different stages of the breeding cycle. We also determined whether variables that influence the value of offspring, such as time of season, size and age of clutch or brood, and time of day and number of visits to the nest, explain variation in the intensity of defence recorded during the egg and nestling stages. Responses to the three stimuli differed significantly, as reed warblers consistently directed their mobbing calls and attacks towards parasites, whereas they were less conspicuous when confronted with models of predators. Reed warblers modulated their responses towards each stimulus in accordance with the threat each posed at a specific nesting stage, whereas they were not affected by other variables relative to their reproductive potential. The churr call, however, was uttered independently of the stimulus, as it was triggered by the mere presence of nestlings in the nest.
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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".