Contributions of Lake Erie and Lake St. Clair Walleye Populations to the Saginaw Bay, Lake Huron, Recreational Fishery: Evidence from Genetic Stock Identification
Bibliographic record
Abstract
Abstract Genetic stock identification analyses were conducted to determine spawning population contributions to the recreational fishery for Walleyes Sander vitreus in Saginaw Bay, Lake Huron. Two spawning population groups were considered: (1) the Tittabawassee River, which has been identified as the largest source of spawning Walleyes for Saginaw Bay; and (2) an aggregate of six spawning populations from Lake Erie and Lake St. Clair that were found to be genetically similar. Overall, the Lake Erie and Lake St. Clair spawning populations were estimated to comprise approximately 26% of the Walleye recreational harvest in Saginaw Bay during 2008–2009. Contribution levels were similar for the 2 years in which genetic samples were collected. Contributions from the Lake Erie and Lake St. Clair spawning populations to the harvest of age-5 and older Walleyes were greater during summer (31.8%; SE = 6.2%) than during late winter and spring (6.0%; SE = 3.7%). Conversely, contributions from the Lake Erie and Lake St. Clair spawning populations to the harvest of age-3 and age-4 fish were fairly similar between seasons (late winter and spring: 31.2%, SE = 6.7%; summer: 41.7%, SE = 5.6%), suggesting that younger Walleyes migrate earlier or reside in Saginaw Bay for extended periods. Our finding that one-quarter of the Saginaw Bay recreational harvest of Walleyes comprises fish from Lake Erie and Lake St. Clair has important management implications, as policies for one lake may have bearing on the other lake—one of the challenges associated with managing migratory fish species. Fisheries management in the Laurentian Great Lakes has a history of being highly coordinated and cooperative among the states and province bordering the individual lakes. Results from this study suggest that cooperation may need to be expanded to account for fish movement between lakes. Received October 28, 2014; accepted February 11, 2015
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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 | 0.000 | 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) | 0.001 | 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".