Variation in nest defense in ducks: methodological and biological insights
Bibliographic record
Abstract
Studies of avian nest defense generally explain only a small proportion of the total variation in defense behavior. We explored two potential methodological sources of variation in nest defense in three species of ducks. One common method of quantifying nest defense rests on the assumption that different components of nest defense (e.g. flushing distance, distraction displays) are highly positively correlated. Defense behaviors we observed in this study were weakly related or unrelated to each other. Thus, the assumption of strong positive covariance between components of nest defense was not supported. We also considered the effect of repeated visits to the same nests on nest defense. Females of all three species took less risk defending their nests with repeated visits, and the effect of visit number on nest defense was greater than the effect of increasing value of nests associated with advancing incubation. Ducks appear to be different from other birds in the consistency with which they alter their nest defense in response to repeated nest visits. We propose that this could be a consequence of having nest predators that return to the vicinity of a nest if they were previously unsuccessful finding the nest, thus making repeated nest visits more dangerous to the ducks. By testing this or other hypotheses it should be possible to go beyond understanding the methodological implications of the effect of repeated visits on nest defense, and use this phenomenon to gain insight into the predator‐prey interactions that underlie nest defense.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".