Estimation of Tag Shedding and Reporting Rates for Lake Erie Jaw-Tagged Walleyes
Bibliographic record
Abstract
Abstract Since 1990, walleyes Sander vitreus in Lake Erie have been tagged annually with jaw tags to better understand the population dynamics and ecological characteristics of individual spawning populations. Although the data collected from this tagging program have been used for a variety of management purposes (e.g., estimating migration patterns, stock intermixing, and mortality rates), there has been only cursory examination of the shedding and reporting rates associated with the program. We used double tagging and high-reward tagging experiments to estimate tag shedding and reporting rates for jaw-tagged walleyes in Lake Erie. Double tagging of walleyes with jaw and passive integrated transponder (PIT) tags suggested that the tagging method and tagging agency contributed to the observed variability in both immediate (within 21 d of tagging) retention and chronic jaw tag shedding rates. Agency-specific model-averaged estimates of immediate tag retention ranged from 95% to 99%. For chronic shedding, model-averaged instantaneous rates (annual) ranged from 0.07 to 0.28. Jaw tag reporting rates, estimated via releases of high-reward tags in 1990 and 2000, varied among tagging years, tagging basins, and commercial and recreational fisheries. In general, tag reporting rates were higher for the recreational fishery (range, 33–55%) than for the commercial fishery (10–17%), and the reporting rates for both fisheries and tagging basins were found to have declined between 1990 and 2000. Uncertainty in the tag reporting rates was greater for the recreational fishery than for the commercial fishery. Our findings will benefit management of the economically important Lake Erie walleye fisheries by providing managers with robust tag shedding and reporting rate estimates, providing more reliable estimates of important dynamic rates (e.g., fishing and natural mortality) by correcting the long-term jaw tagging data set for these previously unaccounted for biases. Received September 13, 2011; accepted November 30, 2011
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".