A Meta‐Analysis of Band Reporting Probabilities for North American Waterfowl
Bibliographic record
Abstract
ABSTRACT Knowledge of band reporting is important for converting band encounter data into estimates of harvest probabilities, which can then be used to assess harvest management goals or estimate population size and other vital rates. Historical estimates of band reporting probabilities have come from reward‐band studies or joint analysis of band recovery and harvest survey data, but there are long gaps between estimates, and most studies have focused exclusively on mallards ( Anas platyrhynchos ). We compiled 337 estimates of band reporting probabilities for North American waterfowl and conducted a Bayesian state‐space analysis to provide a continuous time series of estimated reporting probability from 1948 to 2010. Band reporting probability increased sharply between 1996 and 2000 when toll‐free phone numbers were added to band inscriptions and agencies implemented electronic methods for band reporting, but our analysis also identified gradual long‐term trends in reporting probability throughout the time series. We found little evidence for among‐species variation in reporting probability, but a few species that are widely regarded as trophies by waterfowl hunters (canvasbacks [ Aythya valisineria ], redheads [ Aythya americana ], and northern pintails [ Anas acuta ]) had higher historical reporting probabilities than mallards. We also found little evidence of geographic variation in reporting probabilities, although we confirmed lower reporting probabilities for eastern Canada. We recommend using our estimates of band reporting probabilities and their variances as informed priors in future analyses of band recovery data to fully embrace uncertainty about how this parameter affects estimates of other population parameters. Retrospective studies using parts collection data are needed to explore potential among‐species variation in reporting probabilities during recent decades. © 2019 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 | 0.027 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.025 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".