Application of Otolith Chemistry to Investigate the Origin and State-Straying of Steelhead in Lake Erie Tributaries
Bibliographic record
Abstract
Abstract In Lake Erie, the fishery for steelhead Oncorhynchus mykiss is overwhelmingly dominated by stocking from state agencies in Michigan, New York, Pennsylvania, and Ohio. Managers that stock steelhead may become concerned if a sizeable portion of the fish they stock do not return to the waters in which they were released and instead stray to other states (“state-straying”). During fall 2009, spring 2010, and spring and fall 2015, we evaluated the origin and state-straying of adult steelhead in five annually stocked tributaries of Lake Erie. We also investigated spatial differences in the origin and state-straying of adult steelhead at different stream locations in two tributaries during 2015. Otolith chemistry signatures were first used to discriminate among yearling steelhead from each of the state hatcheries that stock Lake Erie and wild juveniles from Cattaraugus Creek, New York, and the Grand River, Ontario, resulting in a mean jackknifed classification accuracy of 88% (range = 72–100%). Otolith chemistry analysis was then performed on unknown-origin adult steelhead collected in Lake Erie tributaries during the fall and spring spawning runs, and natal sources were identified by using source-specific otolith chemical signatures. State-strays averaged 46% (range = 13–88%) of the adult fish collected, and high percentages of strays were identified in Chautauqua Creek, New York (72%), and Cattaraugus Creek (88%). Steelhead collections in these New York tributaries during a second year revealed that the percentage of strays present was consistently high, and large proportions of strays were identified in both upstream and downstream locations. These results suggest that state-straying is widespread in Lake Erie tributaries and that strays make up a large proportion of New York's Lake Erie tributary fishery. Strategies to reduce straying may include practicing upstream releases and reducing the number of hatchery fish that are released in some states.
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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 | 0.000 | 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".