Stock specific high-seas distribution of maturing sockeye salmon in the North Pacific
Bibliographic record
Abstract
The stock-specific distribution of maturing and adult salmon in the Northeast (NE) Pacific has been a persistent information gap that has prevented us from determining the ocean conditions experienced by individual stocks. This continues to impede understanding of the role of ocean conditions in stock-specific population dynamics. We assessed scale archives for 17 sockeye salmon (Oncorhynchus nerka) stocks covering the entire North Pacific, from the Columbia River to Kamchatka Peninsula, to define salmon locations during their last growing season before returning to their spawning grounds. We used the relationship between δ13C in salmon scales and sea water temperature to estimate salmon distribution based on correlation strength. Significant correlations were found for 13 of the stocks allowing us to define feeding grounds with confidence. Complementary information from δ15N, historical tagging studies, and connectivity analysis were used to further refine distribution estimates. Based on the estimated distributions of the NE Pacific stocks, we suggest a sequence of steps that could result in salmon marine distributions. This study is a first step toward determining stock-specific distributions of salmon in the NE Pacific, and provides a basis for the application of the approach to other salmon scale archives. This information will improve our ability to relate stock dynamics to ocean conditions, ultimately enabling improved stock management. For example, our estimated distributions of Bristol Bay and NE Pacific stocks demonstrated that they occupy different areas with a number of the former being distributed in the high productivity shelf waters of the Aleutian Islands and Bering Sea. This may explain while these stocks seem to have responded differently to changes in ocean conditions, and the long term trend of increased productivity of Bristol Bay sockeye.
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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 | 0.000 |
| 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".