Experimental evidence that nesting ducks use mammalian urine to assess predator abundance
Bibliographic record
Abstract
Nest predation is a major cause of reproductive failure for many birds; as a consequence, birds that can assess the abundance of predators and avoid nesting where they perceive predation risk to be high should be favored. For dabbling ducks, mammals are important predators on nests and incubating females. Many mammals use urine for marking territories. Because ducks may be able to detect mammalian urine either by ultraviolet light reflectance or by odor, we hypothesized that ducks may be able to assess the abundance of mammalian predators from their urine and thereby avoid nesting where mammals are abundant. We simulated increased predator abundance on experimental plots by using Red Fox (Vulpes vulpes) urine to make artificial scent marks and used water in a similar fashion on control plots. On 16 pairs of plots over 2 years, fewer ducks nested on experimental plots than on control plots (97 vs. 143 nests). These results suggest that birds can assess predator abundance and use the information to choose where to 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 | 0.002 |
| 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".