Estimating internal transmitter and external tag retention by Walleye in the Laurentian Great Lakes over multiple years
Bibliographic record
Abstract
Abstract Objective Both electronic tags (e.g., acoustic and radio transmitters) and conventional external tags are used to evaluate movement and population dynamics of fish. External tags are also sometimes used to facilitate the recovery of internal electronic tags or other instrumentation because healing can make it difficult to identify fish with internal tags based on appearance alone. With both tag types, tag shedding and failure of electronic tags can affect accuracy and precision of study results. Methods We used a decade (2011–2021) of recapture data for Walleye Sander vitreus tagged in the Laurentian Great Lakes, where fish were double- or triple-tagged with external tags (T-bar, loop, or internal anchor tags) and internal acoustic transmitters, to quantify external tag and internal transmitter shedding and transmitter failure rates. Result In total, 1125 (33%) Walleye were recovered that had retained at least one external tag or internal transmitter. No confirmed cases of transmitter shedding were observed; 15 of 899 transmitters (2%) that were checked for functionality failed prior to the expected battery expiration. The retention of external T-bar tags 1 year after release differed depending on whether the tag was placed anterior or posterior to the secondary dorsal fin (anterior, fish length = 420 mm: 73% retention; anterior, fish length = 700 mm: 73%, posterior: 63%) but was <26% after 4 years for both tag positions and fish sizes. Internal anchor tags had an 88% 1-year retention probability and 81% 4-year retention probability. Loop tags had the highest 1-year retention (89%) but after 4 years retention (28–34% depending on agency) was comparable to that of T-bar tags. Conclusion Better understanding of tag retention characteristics through long-term tagging studies such as this can inform study design, be considered in model design, and ultimately improve inferences from mark–recapture studies.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".