Nest stage, wind speed, and air temperature affect the nest defence behaviours of burrowing owls
Bibliographic record
Abstract
The effect of nest stage on nest defence responses has been fairly well established but the impact of weather conditions has been largely ignored. We examined the effects of nest stage, number of previous visits, wind speed, and air temperature on burrowing owl (Athene cunicularia (Molina, 1782)) defence of nests from a human intruder. We found that burrowing owls changed nest defence tactics from retreat behaviour to more confrontational behaviour once eggs hatched. Aggressiveness was significantly reduced as wind velocity increased and when temperatures were warmer. We found no evidence for a change in owl defence behaviour with the number of previous visits to a nest. Although not statistically significant, there was a tendency for burrowing owls to allow closer approaches and to not retreat as far once eggs had hatched. Wind speed did not have an effect on retreat or approach distances, and owls allowed us to get significantly closer to the nest before retreating when air temperatures were warm. There are a multitude of factors that could affect nesting success and thus fitness of birds, but our study shows that routine climatic events such as warm weather had a measurable impact on how a bird defended its reproductive investment.
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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 | 0.001 |
| 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".