Manipulated density of adult mallards affects nest survival differently in different landscapes
Bibliographic record
Abstract
Breeding success in many birds including wildfowl is mainly determined by nest predation. Few studies address cues used by predators to find duck nests, and the same is true for spacing patterns that ducks might use to reduce predation. We designed a crossover experiment in agricultural and forested settings to test the assumption that nest predation rate is related to density of adult birds on a lake. We used introduced wing-clipped mallards ( Anas platyrhynchos L., 1758) to increase local pair density and semi-natural nests to assess predation rate. Depredation patterns were analyzed by model fitting in program MARK, using introduction and landscape type as main effects and abundance of avian predators and wild waterbirds as covariates. Depredation was higher at agricultural lakes than at forest lakes. Nest survival decreased with increasing abundance of wild waterfowl, whereas it tended to increase with the abundance of “other waterbirds”. There was a landscape-dependent effect of increased mallard pair density: positive at agricultural lakes and negative at forest lakes. Avian predators found 91% of depredated “known-predator” nests at agricultural lakes and 25% at forest lakes; mammals found 9% at agricultural lakes and 75% at forest lakes. The landscape-dependent density effect may in part be due to different predator communities in these landscape types.
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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".