Nesting Mottled Duck ( Anas fulvigula ) selection of ungrazed grasslands in southwestern Louisiana is associated with increased nest survival
Bibliographic record
Abstract
Nest site selection is a discrete and often repeated choice, and individuals should select nest sites that maximize reproductive success and thus increase fitness. Mottled Ducks (Anas fulvigula) are a non-migratory species that inhabits the Gulf Coast of the United States year-round and therefore have the ability to constantly evaluate habitat to make well-informed nest site choices compared to migratory species. Mottled Duck populations have declined over the last decade and a better understanding of nest site selection and its relationship to nest survival is a top research priority. We deployed GPS transmitters on 148 females across three breeding seasons to evaluate nest site habitat selection and nest survival. We observed 30 nest attempts and found females selected sites in diverse landscapes, but Mottled Ducks preferred old fields and pasture relative to other habitats. High vegetation density surrounding the nest bowl had a positive influence on nest survival. We found that females were more likely to renest when the initial nest failure occurred earlier in the incubation period. Our results emphasize the importance of preserving tall, dense vegetation in upland habitats. Additionally we recommend that prescribed burns are timed to ensure adequate vegetative cover for Mottled Ducks by the onset of nesting in March.
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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".