Survival and harvest of Atlantic Flyway resident population Canada Geese
Bibliographic record
Abstract
ABSTRACT Resident Population Canada geese ( Branta canadensis ) are a valuable natural resource, but at high densities they create problems by colliding with vehicles, damaging crops, and fouling parks with feces. Effective management of these geese could be improved with knowledge of demographic rates, especially survival. We used band recovery data from 2005 to 2012 to estimate temporally and spatially explicit survival and recovery rates of Atlantic Flyway Resident Population Canada geese. We analyzed the data in Program MARK and found evidence that survival and recovery varied by age, state of banding, and year. We present state–age–year survival, recovery, and harvest rates from all states. Model‐averaged estimates of adult survival ranged from 0.62 to 0.87 and had high precision for most states. Estimates of survival of juvenile geese were generally higher than those for adult geese, but they were less precise and more variable among states. Based on estimates of survival and recovery rates, the average annual harvest rate of adult geese was 13.5% and ranged from 3.1% in North Carolina to 20.1% in Pennsylvania, USA. Harvest rates of juvenile geese were not significantly different from those of adult geese and averaged 15.3%. The estimated survival and harvest rates can be incorporated into population models to assess potential effectiveness of various management strategies for Resident Population Canada geese. © 2015 The Wildlife Society.
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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".