Lesser Scaup Breeding Probability and Female Survival on the Yukon Flats, Alaska
Bibliographic record
Abstract
ABSTRACT Information on the ecology of waterfowl breeding in the boreal forest is lacking, despite the boreal region's importance to continental waterfowl populations and to duck species that are currently declining, such as lesser scaup ( Aythya affinis ). We estimated breeding probability and breeding season survival of female lesser scaup on the Yukon Flats National Wildlife Refuge, Alaska, USA, in 2005 and 2006. We captured and marked 93 lesser scaup with radiotransmitters during prelaying and nesting periods. Although all marked lesser scaup females were paired throughout prelaying and incubation periods, we estimated breeding probability over both years as 0.12 (SE = 0.05, n = 67) using telemetry. Proportion of lesser scaup females undergoing rapid follicle growth at capture in 2006 was 0.46 (SE = 0.11, n = 37), based on concentration of yolk precursors in blood plasma. By combining methods based on telemetry, yolk precursors, and postovulatory follicles, we estimated maximum breeding probability as 0.68 (SE = 0.08, n = 37) in 2006. Notably, breeding probability was positively related to female body mass. Survival of female lesser scaup during the nesting and brood‐rearing periods was 0.92 (SE = 0.05) in 2005 and 0.86 (SE = 0.08) in 2006. Our results suggest that breeding probability is lower than expected for lesser scaup. In addition, the implicit assumption of continental duck‐monitoring programs that all paired females attempt to breed should be reevaluated. Recruitment estimates based on annual breeding‐pair surveys may overestimate productivity of scaup pairs in the boreal forest.
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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 | 0.001 | 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".