Mitochondrial DNA Variation and Mixed-Stock Analysis of Recreational and Commercial Walleye Fisheries in Eastern Lake Erie
Bibliographic record
Abstract
The relative contributions of five spawning stocks of walleye Stizostedion vitreum to three recreational derbies and a commercial fishery operating in eastern Lake Erie in 1995 and 1996 were estimated by analyzing mitochondrial DNA (mtDNA) variation and by using a maximum likelihood model. Simulations of fishery mixtures (N = 100) were used to assess the performance of the likelihood model for estimating contributions from three baselines composed of the five Lake Erie spawning stocks: Chickenolee Reef and Huron River (western basin), Grand River (eastern basin), Van Buren Bay and Smokes Creek (eastern basin). Differences between the simulated mixtures and the mean of the estimates revealed that the model differentiated among varying simulated contributions from the three baselines. Bias among the estimates was greatest when a single baseline contributed entirely to the simulated mixture. Mixed-stock analysis (MSA) of the eastern basin fisheries suggested that the western basin baseline was a major contributor to both the recreational (0.63 ± 0.10) and the commercial (0.81 ± 0.14) fisheries. The eastern baseline contribution estimates were not significantly different from zero, with the exception of the Grand River baseline contribution to the recreational derbies (0.16 ± 0.08). The results are preliminary to show that mtDNA variation and a maximum likelihood model can be used to estimate the relative contributions of walleye stocks to the mixed fisheries of eastern Lake Erie. Further research using comprehensive baselines, including all potential contributing stocks, is recommended before the MSA information presented is used directly for the management of walleyes 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".