Band reporting probabilities for mallards recovered in the United States and Canada
Bibliographic record
Abstract
ABSTRACT Reliable estimates of annual harvest rates are required for the implementation of mallard (Anas platyrhynchos) adaptive harvest management decision frameworks. Because not all standard bands recovered during the hunting season are reported, band reporting probabilities are needed to estimate mallard harvest rates. Information from birds recovered with bands that notify finders of a reward (i.e., reward bands) can be used to estimate band reporting rates. We analyzed reward banding data for 3 stocks of mallards to estimate reporting probabilities that can be used to estimate harvest rates for birds recovered with toll‐free or web‐address bands. Specifically, we explored spatial variability in reporting probabilities, and assessed whether reporting probabilities varied among years. Our analysis indicated that reporting probabilities varied among the 4 Flyways, eastern Canada, and western Canada and Alaska. We had difficulty interpreting temporal fluctuations and found little evidence for any meaningful trends in reporting rates between 2002 and 2010. We recommend that reporting probabilities of 0.67 in the Atlantic Flyway, 0.81 in the Mississippi Flyway, 0.70 in the Central Flyway, 0.76 in the Pacific Flyway, 0.50 in eastern Canada, and 0.57 in western Canada and Alaska be used to estimate harvest probabilities for birds recovered in these regions. © 2013 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".