Survival rates and harvest patterns of Ohio‐Banded Canada geese
Bibliographic record
Abstract
ABSTRACT Growth of temperate breeding Canada goose ( Branta canadensis maxima ) populations remains a challenge for agencies that seek to balance social acceptance with demand for hunting opportunity from constituents. Harvest regulation is the principle means by which federal and state agencies attempt to keep populations in balance with their environment. Band recovery data and aerial surveys are used to monitor populations and evaluate population control efforts. Greater than 140,000 temperate‐breeding Canada geese were banded in Ohio, USA, from 1990 to 2015. We used Brownie dead‐recovery models to estimate survival rates as a function of time, age, urban–rural status, winter weather severity, and hunting regulations. We derived annual direct‐recovery rates by age and urban–rural cohorts. We mapped all recoveries of Ohio‐banded geese to investigate changes in harvest distribution over time. The highest‐ranked model that explained survival of Ohio‐banded geese had urban–rural status, age, and winter weather severity effects. Survival rates were lower during severe winters for adult rural geese, adult urban geese, and hatch‐year urban geese; however, hatch‐year rural geese had greater survival rates during severe winters. Direct recovery rates of all geese remained stable over the duration of the study (1990–2015), and there was a shift eastward in distribution of band recoveries over time. Survival rates of Ohio‐banded Canada geese appear to be largely unaffected by annual harvest regulations. Furthermore, long‐term moderation of winter weather in Ohio could result in increased adult goose survival, requiring additional management actions to temper population growth. © 2018 The Wildlife Society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.006 | 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 teacher head, 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".