Variation in Breeding Season Survival of Female Harlequin Ducks
Bibliographic record
Abstract
ABSTRACT Quantifying sources of variation in demographic rates can provide insight into processes underlying population dynamics and subsequently direct wildlife conservation. In the context of avian life history, understanding patterns of variation in survival rates of breeding females is particularly relevant because this cohort often has a disproportionately large effect on population dynamics. We estimated survival probability for 144 adult female harlequin ducks ( Histrionicus histrionicus ) that we marked with radiotransmitters and tracked at 4 breeding areas in western North America. Model selection results indicated both regional and temporal variation in survival rates, with most mortality attributed to predation. Cumulative survival probability (±SE) during the 100‐day study period was lower at 2 sites in the Rocky Mountains of Alberta, Canada (AB1 and AB2: 0.75 ± 0.11) than in the Coast Mountains of British Columbia, Canada (BC: 0.88 ± 0.08) or the Cascade Mountains of Oregon, USA (OR: 0.89 ± 0.08). Survival also was lower during incubation than nest‐initiation or brood‐rearing stages at all 4 study areas. In comparison to other annual cycle stages and locations, harlequin duck mortality rates were highest on the breeding grounds, suggesting that management actions designed to reduce mortality during breeding would achieve meaningful population‐level benefits.
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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".