The distribution and nest survival of Giant Canada Geese breeding in Iowa
Bibliographic record
Abstract
Updated measurements of Canada goose distribution and nest survival are essential to develop and evaluate management strategies. Iowa's protocols for monitoring the Canada goose breeding population use a stratified random sampling method to select square-mile sections to be surveyed by helicopter. Precise population estimates require that the universe of survey plots be accurately stratified. I provided a more statistically rigorous method of stratifying Iowa's square-mile sections by developing a model to predict Canada goose breeding pair densities by incorporating updated National Wetlands Inventory data and previous breeding population survey data. I found that breeding pairs were best predicted by the wetland types, number of wetlands, area of each wetland type, and a quadratic of the area of each wetland type in each section, as well as an interaction between the wetland types and the area of each wetland type, and random effects for observations and sections. The model indicated that goose densities are highest at large semi-permanent marshes. Reliable estimates of Canada goose nest survival allow management agencies to evaluate available nesting habitats and determine appropriate management techniques. I monitored Canada goose nests at five state-managed wetland complexes to determine how nesting habitat influenced nest survival rates at rural wetlands in north-central Iowa. I found that nest structures produced significantly higher nest survival than nests on islands and muskrat houses. I also found that shallow lake renovation activities at Rice Lake Wildlife Management Area, which involved manipulating the water level, had a negative impact on Canada goose nest survival.
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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".