Simulating Effects of Nonintrogressive Hybridization with a Stocked Hatchery Strain of Rainbow Trout on the Sustainability and Recovery of Naturalized Steelhead Populations in Minnesota Waters of Lake Superior
Bibliographic record
Abstract
Abstract A model was developed to explore the impacts of nonintrogressive hybridization with a stocked hatchery strain of rainbow trout Oncorhynchus mykiss (Kamloops strain [KAM]) on the sustainability and recovery of naturalized steelhead (anadromous rainbow trout) populations in Minnesota tributaries of Lake Superior. The model was used to assess the extinction risk of Lake Superior steelhead over a 50-year period based on multiple KAM stocking scenarios, initial population sizes, and levels of assortative mating. No extinctions occurred in simulated steelhead populations regardless of initial size after a one-time introduction of KAM; however, the risk of extinction due to nonintrogressive hybridization increased dramatically for scenarios involving annual stocking of KAM. The level of assortative mating among KAM and steelhead greatly influenced the risk of steelhead population decline or extinction for all scenarios. Results of the model support the contention that nonintrogressive hybridization could be an impediment to the sustainability and recovery of the steelhead population in Lake Superior. Received June 14, 2010; accepted July 20, 2011
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".