Acoustic telemetry as a potential tool for mixed-stock analysis of fishery harvest: a feasibility study using Lake Erie walleye
Bibliographic record
Abstract
Understanding stock composition is critical for sustainable management of mixed-stock fisheries. When natural markers routinely used for stock discrimination fail, alternative techniques are required. We investigated the feasibility of using acoustic telemetry to estimate spawning population contributions to a mixed-stock fishery using Lake Erie’s summer walleye (Sander vitreus) recreational fishery as a case study. Postrelease survival was estimated after tagging and used to inform simulations to evaluate how contribution estimates could be affected by survival, sample size, and expected population contributions. Walleye experienced low short-term survival after tagging, but showed higher survival after 100 days, likely allowing fish to return to spawning areas the following spring. Based on simulations, accuracy and precision of population composition increased with an increase in the number of tagged fish released, and both appeared to stabilize when ≥200 tagged fish were released. Results supported the feasibility of using acoustic telemetry to estimate spawning population contributions to mixed-stock fisheries in Lake Erie.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".