Microsatellite Population Genetics and Juvenile Skeena Sockeye Migration
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.Sockeye salmon (Oncorhynchus nerka) support the most valuable commercial fishery of the Canadian Pacific coast. We used microsatellite DNA to characterize aspects of sockeye salmon ecology of the Skeena River. The different Skeena sockeye populations which originate from approximately 28 rearing lakes, migrate to sea in May and June. Sockeye salmon smolts migrate earlier from lakes that are closer to the coast than from lakes that are further inland. While it has long been known that the different populations of adult sockeye salmon return to the Skeena River, and to Babine Lake tributaries in a characteristic sequence, our results demonstrate that the phenology of downstream migration for different populations of sockeye salmon smolts is also structured by timing and geography. Babine Lake, which produces approximately 80% of all Skeena sockeye salmon, may be divided into several subpopulations. Genetic analysis of samples collected in 2014 shows that juvenile sockeye originating from tributaries to different sections of Babine Lake had distinctive timing. 84.3% of the earlier migrants came from populations that spawn in the northern section closest to the lake outlet, while 87.6 % of later migrants came from populations that spawn in the middle and upper sections of the lake.
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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 | 0.001 | 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) | 0.005 | 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".