Pacific Salmon: Ecology and Management of Western Alaska’s Populations
Bibliographic record
Abstract
<em>Abstract.</em>—The genetic population structures of chum <em>Oncorhynchus keta</em>, Chinook <em>O. tshawytscha</em>, coho <em>O. kisutch</em>, sockeye <em>O. nerka</em>, and pink <em>O. gorbuscha </em>salmon within the AYK region are described based on available published and unpublished information. The most detailed genetic data were for chum salmon where major groups included: (1) summer-run fish returning to coastal rivers and the lower reaches of the Yukon and Kuskokwim Rivers, (2) upper Yukon River, and (3) upper Kuskokwim River fall-run populations. AYK Chinook and coho salmon populations showed similar patterns of differentiation within the Yukon and Kuskokwim Rivers, although each species had quite different spatial separation and timing. Based on unpublished genetic data from AYK sockeye salmon populations, Norton Sound populations were grouped together and were distinct from ten other areas within the Yukon and Kuskokwim drainages which had affinities with Bristol Bay populations. Available pink salmon data were insufficient to estimate population structures. Similarity of AYK and Susitna River chum and Chinook salmon populations suggest a common ancestry that may reflect an historical connection of these drainages. Low species-wide indices of among-population genetic variation (<EM>F<sub>ST</sub></EM>) in chum and pink salmon suggest that regionally based conservation strategies for these species will be effective. In contrast, Chinook, coho, and sockeye salmon had higher <EM>F<sub>ST</sub> </EM>values and require population-specific strategies. Genetic stock identification methods (mixed stock analysis) provided valuable estimates of oceanic distributions of AYK chum salmon, and in-season estimates of chum, Chinook, and coho salmon stocks migrating within the Yukon and Kuskokwim Rivers. The genetic information now known about salmon in the AYK region will help the formulation and design of future investigations, and will ultimately promote a better understanding, management, and conservation of AYK salmon.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".