Pacific Salmon: Ecology and Management of Western Alaska’s Populations
Bibliographic record
Abstract
Abstract.—The Bering Sea ecosystem can be considered an extension of the North Pacific Ocean ecosystem, especially with respect to coastal and upper ocean conditions. Because marine measurements are more numerous in the North Pacific, it is possible to deduce clear, historical changes in this large-scale marine ecosystem, and to apply them to the Bering Sea and Arctic-Yukon-Kuskokwim fisheries. To address recent changes in the marine conditions in the Northeast Pacific, we place them into centennial and millennial contexts. Ocean monitoring of temperature and salinity versus depth profiles at the mouth of Resurrection Bay near Seward, Alaska (station GAK1) since 1970 reveals significant linear temperature increases of about 1°C throughout the 250 m water column. Concurrent with this warming, the salinity of the upper layer (0–100 m) is decreasing while the lower layer (100–250 m) salinity is increasing. In the recent decade, storminess over the Gulf of Alaska has also increased. All of these changes are consistent with a conceptual model that includes ocean circulation, precipitation, glacial melting, and wind forcing. The model has positive feedbacks, suggesting that changes in the salinity are transporting more heat pole-ward, leading to increased storminess, precipitation, glacial melting, and decreased upper layer salinity and more heating. Superimposed on these linear trends are briefer fluctuations (months to years) that might influence the marine ecosystem. Similar trends in hydrography were reported for briefer records in the Bering Sea and Bering Straits. Sitka air temperatures since 1828 and Gulf of Alaska tree ring data since 700 A.D. suggest that periodic “regime shifts” have occurred at 25–30 year intervals since 1700. The last regime shift (warming) took place in 1977. These changes are likely to impact both marine and freshwater ecosystems used by salmon from the Arctic-Yukon-Kuskokwim region and they may play a role in controlling the growth and abundance of these populations.
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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) | 0.001 | 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".