Band Reporting Probabilities of American Black Ducks in Eastern North America 2017–2019
Bibliographic record
Abstract
Abstract American black ducks Anas rubripes (hereafter, black ducks) are an important game species in the eastern United States (U.S.) and Canada that declined between the 1950s and 1990s, which resulted in the implementation of restrictive hunting regulations in the United States and Canada. Black duck harvest is managed by the Black Duck International Adaptive Harvest Management (BDAHM) strategy that was developed between Canada and the U.S. The strategy requires that the black duck population be maintained at a level that is commensurate with legal mandates, provides for use appropriate for the habitat carrying capacity, and managed in a manner that maintains equitable access (between Canada and the U.S.) to the black duck resource. Fulfilling these mandates requires unbiased country-specific harvest probability estimates, which in turn require estimates of band reporting probability, that is, the probability that a hunter who harvests a banded bird will report it to the North American Bird Banding Program (NABBP; i.e., either the US Bird Banding Lab (BBL) or Canadian Bird Banding Office (BBO)). We conducted a reporting probability study during the 2017-18, 2018-19, and 2019-20 hunting seasons, using reward bands to estimate continental and country-specific band reporting probabilities. The continental (pooled) band reporting probability was 0.80 (0.660–0.945, 95% confidence interval). Band reporting probability was lower in Canada 0.65 (0.487—0.821, 95% CI) than in the United States 1.00 (0.978–1.022, 95% CI), but increased in both countries since they were last estimated in the 2000s. Increased reporting probability, as well as the difference in reporting between the two countries, should be accounted for to most effectively meet the objectives of the BDAHM strategy.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".