Pacific Salmon: Ecology and Management of Western Alaska’s Populations
Bibliographic record
Abstract
Abstract.—Data from high seas tagging experiments (external tags, coded-wire tags, electronic data storage tags) provide the only direct information on the distribution, biology, and ecology of immature and maturing Arctic-Yukon-Kuskokwim (AYK) salmon (Oncorhynchus spp.) migrating in the North Pacific Ocean and Bering Sea. Variation in the spatial and temporal distribution of tagging effort largely reflects changes in international salmon treaty research priorities over the past 52 years (1954–2006). Results of tagging studies indicate that in spring maturing AYK pink O. gorbuscha and coho O. kisutch salmon and immature and maturing AYK sockeye O. nerka and chum O. keta salmon are distributed primarily in the northeastern North Pacific Ocean and Gulf of Alaska, and in summer their distribution shifts to the west in the Gulf of Alaska and to the north and west in the Bering Sea. Immature and maturing AYK Chinook salmon O. tshawytscha are distributed in the eastern Bering Sea in winter, and immature Chinook salmon are distributed in the central and western Bering Sea in summer. Depth data from electronic tags indicated that Chinook and chum salmon have the deepest vertical distributions among the salmon species. Swimming depths might remain relatively constant across water masses and ocean areas. Bioenergetic simulations indicated that AYK salmon experiencing increased mean summer temperatures in the Bering Sea could suffer reduced growth at all age-maturity stages unless prey availability or prey energy density increased commensurately. Published conceptual models of the high seas distribution and migration patterns of AYK salmon need to be updated with new information from tagging, scale pattern, and genetic studies. New dynamic models would be useful for predicting climate-induced changes in carrying capacity, growth and survival, exploitation by marine fisheries, and timing of adult returns to the AYK region.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".