Effects of repeat sampling in the U.S. Waterfowl Parts Collection Survey
Bibliographic record
Abstract
Age ratio estimates obtained annually by the Cooperative Waterfowl Parts Collection Survey (PCS) serve as important estimates of annual waterfowl recruitment. To determine if age and sex ratios are biased due to repeat sampling of hunters across years, I examined PCS data collected from 1991-2000. Mean seasonal harvest increased with number of consecutive years hunters responded to the PCS. Proportions of juveniles in the mallard (Anas platyrhynchos) harvest and harvest of all species combined decreased with increasing seasonal harvest level. Proportions of males in the harvest increased with increasing harvest level. Proportions of juveniles in the harvest of hunters responding to the PCS 3 and 4 consecutive years were slightly lower than proportions in the harvest of hunters responding only once or twice. Proportion of males in the mallard harvest increased with number of years hunters remained in the PCS. Although large sample sizes produced statistically significant effects (P < 0.05) of seasonal harvest and repeat sampling, actual differences in predicted proportions were quite small. My results suggest that age and sex ratio estimates remain relatively unaffected by repeat sampling in the PCS.
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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 | 0.037 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".