Assessment of bias in US waterfowl harvest estimates
Bibliographic record
Abstract
Context North American waterfowl managers have long suspected that waterfowl harvest estimates derived from national harvest surveys in the USA are biased high. Survey bias can be evaluated by comparing survey results with like estimates from independent sources. Aims We used band-recovery data to assess the magnitude of apparent bias in duck and goose harvest estimates, using mallards (Anas platyrhynchos) and Canada geese (Branta canadensis) as representatives of ducks and geese, respectively. Methods We compared the number of reported mallard and Canada goose band recoveries, adjusted for band reporting rates, with the estimated harvests of banded mallards and Canada geese from the national harvest surveys. We used the results of those comparisons to develop correction factors that can be applied to annual duck and goose harvest estimates of the national harvest survey. Key results National harvest survey estimates of banded mallards harvested annually averaged 1.37 times greater than those calculated from band-recovery data, whereas Canada goose harvest estimates averaged 1.50 or 1.63 times greater than comparable band-recovery estimates, depending on the harvest survey methodology used. Conclusions Duck harvest estimates produced by the national harvest survey from 1971 to 2010 should be reduced by a factor of 0.73 (95% CI = 0.71–0.75) to correct for apparent bias. Survey-specific correction factors of 0.67 (95% CI = 0.65–0.69) and 0.61 (95% CI = 0.59–0.64) should be applied to the goose harvest estimates for 1971–2001 (duck stamp-based survey) and 1999–2010 (HIP-based survey), respectively. Implications Although this apparent bias likely has not influenced waterfowl harvest management policy in the USA, it does have negative impacts on some applications of harvest estimates, such as indirect estimation of population size. For those types of analyses, we recommend applying the appropriate correction factor to harvest estimates.
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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 | 0.071 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".