Seasonal Stock‐Specific Migrations of Juvenile Sockeye Salmon along the West Coast of North America: Implications for Growth
Bibliographic record
Abstract
Abstract Knowledge of the migratory habits of juvenile Pacific salmon Oncorhynchus spp. is required to test the hypothesis that ocean food resources are a limiting factor in their production. Using DNA stock identification techniques, we reconstructed the regional and seasonal changes in the stock composition of juvenile sockeye salmon O. nerka (n = 4,062) collected from coastal Washington to the Alaska Peninsula in coastal trawl surveys from May to February 1996–2007. Individuals were allocated to 14 regional populations. The majority were allocated to stocks from the Fraser River system (42%), while west coast Vancouver Island stocks accounted for 15% of the total catch; Nass and Skeena River sockeye salmon constituted 14% and Rivers Inlet 6% of the total. The remainder of the stocks identified individually contributed less than 5% of the sockeye salmon analyzed. These proportions generally reflected the abundance of those populations. In spring and summer, the majority of fish were caught in close proximity to their rivers of origin, lending further support to the allocations. By fall, sockeye salmon were caught as far north and west as the Alaska Peninsula, the majority being caught from central British Columbia to Southeast Alaska. Juvenile sockeye salmon generally disappeared from the coast by winter, suggesting dispersion into the Gulf of Alaska. Within each region, the proportional stock composition changed as the seasons progressed, with northward (and in some cases, rapid) migration along the coast. We also demonstrated stock‐specific differences in migration patterns. For each stock identified, body size and energy density were higher at northern latitudes, suggesting that there is an environmental or food web influence on growth or that faster growing fish initiated their northward migration earlier.
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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) | 0.001 | 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".