Estimation of Genetic Diversity within and among Populations of <i>Oncorhynchus mykiss</i> in a Coastal River Experiencing Spatially Variable Hatchery Augmentation
Bibliographic record
Abstract
Abstract The allelic variation at 10 microsatellite loci was assayed in potentially wild rainbow trout Oncorhynchus mykiss and steelhead (anadromous rainbow trout) collected from 11 tributaries and three upper main‐stem river sites (n = 547) in the Kitimat River, central British Columbia, and compared with the variation in steelhead from areas within the lower river (n = 333), where a hatchery has operated since 1984. The objective was to see whether the genetic structure of O. mykiss in the upper river was influenced by hatchery‐reared fish stocked in the lower river. Measures of genetic diversity indicated that tributary and upper‐river diversity were similar to what has previously been documented for wild populations. The level of genetic subdivision (θ) was significant (θ = 0.031), indicating that genetic structure exists, and was higher than that among sites located in the lower main‐stem river where the hatchery operates (θ = 0.004). Bayesian assignment clustering suggested the existence of a genetic structure (K = 3) in O. mykiss in the upper river. The overall spatial pattern, however, identified no clearly separate genetic populations; rather, the genetic structure appeared to be an overlapping mosaic of modestly genetically divergent localities. We conclude that indigenous wild O. mykiss populations exist in the tributaries and the upper main‐stem river and its tributaries. These upper‐river populations appear to have retained genetic diversity and differentiation despite extensive releases of hatchery fish in the lower river.
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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 | 0.000 | 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".