Variation of Amplified Fragment Length Polymorphisms in Yukon River Chum Salmon: Population Structure and Application to Mixed‐Stock Analysis
Bibliographic record
Abstract
Abstract The population structure of fall‐run Yukon River chum salmon Oncorhynchus keta has been studied previously using allozyme, microsatellite, and mitochondrial markers. However, genetically similar populations from tributaries near the U.S.‐Canadian border render mixed‐stock analyses (MSAs) difficult in the fisheries from lower portions of the Yukon River; MSA simulation apportionment estimates are less than 90% accurate for the border region divided by country of origin. To increase the accuracy and precision of contribution estimates to harvests in the Yukon River and to improve our understanding of the population structure of fall‐run chum salmon, we investigated the variation of amplified fragment length polymorphisms (AFLPs). Our results show that Yukon River chum salmon populations are structured by both seasonal race and geographic region. As expected, the MSA is most successful when mixtures are allocated to geographic regions. Both AFLP and microsatellites have better than 80% apportionment accuracy in MSA simulations for the U.S. and Canadian border regions, but neither approach clearly or consistently outperforms the other. In general, the population structure resolved by AFLP is similar to that observed for other genetic markers. Relatively weak population divergence, rather than shortcomings of the previously studied genetic marker systems, appears to be the limiting factor in attaining high levels of accuracy and precision in MSA.
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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 | 0.002 |
| 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 | 0.000 |
| 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".