Pacific Salmon: Ecology and Management of Western Alaska’s Populations
Bibliographic record
Abstract
Abstract.—In this study, we used genetic and demographic data to estimate and evaluate an indicator of genetic health, the effective number of breeders per year (Nb), in Chinook salmon Oncorhynchus tshawytscha populations from western Alaska. Many of these populations show male-biased (70%–85%) sex ratios. Four such populations were examined: two from the Gisasa and Tozitna Rivers in the Yukon River drainage and two from the Tuluksak and Kwethluk Rivers in the Kuskokwim River drainage. Our objectives were to: 1) evaluate the genetic health of each population, and 2) infer the influence of annual fluctuations in census size, male-biased sex ratio, and variance in family size on Nb. Four genetic estimates of Nb were computed for each population to account for possible bias when using low frequency alleles. The lowest Nb estimates ranged from 225 (Tozitna River) to 4,859 fish (Kwethluk River) and were not indicative of a high risk of long-term loss of genetic diversity. Demographic and genetic estimates of the ratio Nb/N, where N is census size, suggested the observed sex ratio bias is unlikely to adversely impact genetic diversity at current population sizes. Variation in family size within each population likely had the largest affect on Nb over the time period examined. However, the time period was relatively short (3–14 years) and the estimates of Nb, could decline if populations show large future fluctuations, including significant decreases, in census size.
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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 | 0.000 |
| 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".