Canada goose nest survival at rural wetlands in north‐central Iowa
Bibliographic record
Abstract
ABSTRACT The last comprehensive nest survival study of the breeding giant Canada goose ( Branta canadensis maxima ) population in Iowa, USA, was conducted >30 years ago during a period of population recovery, during which available nesting habitat consisted primarily of artificial nest structures. Currently, Iowa's resident goose population is stable and nests in a variety of habitats. We analyzed the effects of available habitat on nest survival and how nest survival rates compared with those of the expanding goose population studied previously to better understand how to maintain a sustainable Canada goose population in Iowa. We documented Canada goose nest survival at rural wetland sites in north‐central Iowa. We monitored 121 nests in 2013 and 149 nests in 2014 at 5 Wildlife Management Areas (WMAs) with various nesting habitats, including islands, muskrat ( Ondatra zibethicus ) houses, and elevated nest structures. We estimated daily nest‐survival rate using the nest survival model in Program MARK. Survival was influenced by year, site, stage, presence of a camera, nest age, and an interaction between nest age and stage. Nest success rates for the 28‐day incubation period by site and year combination ranged from 0.10 to 0.84. Nest survival was greatest at sites with nest structures (β = 17.34). Nest survival was negatively affected by lowered water levels at Rice Lake WMA (2013 β = −0.77, nest age β = −0.07). Timing of water‐level drawdowns for shallow lake restorations may influence nest survival rates. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.
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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 | 0.001 | 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".