Climate Change and Pacific Salmon Productivity on the Russian Far East
Bibliographic record
Abstract
Climate change impact is a mainstream topic in Pacific salmon stock dynamics research.Numerous studies analyze correlations of some salmon species or stock abundance conditions with one or another climate index.Meanwhile, it is evident that no one factor impacts salmon or their environment separately from other elements of the salmon ecosystem.To understand the importance of the contributions of the main physical elements into changing environmental conditions of salmon ecosystems in the North Pacific Ocean, we tried to evaluate correlations between commercial catches of several salmon species with the most popular climate indices.These indices characterize large-scale meteorological, oceanographic, and cosmo-physical processes defining the Earth's climate.Climate change impacts on Pacific salmon (pink, chum, and sockeye) productivity was assessed based on long-term fisheries statistics and dynamics of 18 climate indices using stepwise multivariate regression analysis.Three regional stock groups were analyzed: Eastern Kamchatka and Chukotka (BPG), Western Kamchatka and continental coast (NOG), and Sakhalin, Kuriles, Amur River, and Primorye (SOG).We analyzed salmon catch dynamics in numbers to avoid an influence of fluctuating average salmon body weight.Pink salmon heavily contribute to the total Russian commercial salmon catch, and its predominance is illustrated further when the numbers from catches are reviewed (Fig. 1).Portions of chum and sockeye salmon increased in the last few years, when their commercial catches by Russia regularly reached 100,000 metric tons for chum and 45,000 metric tons for sockeye.If we consider salmon catch dynamics by regional groups and by species, the three selected regional groups contributed comparable portions of the total catch.While the left parts of histograms demonstrate significant interannual variability determined by the interchange of odd-and even-year pink salmon broodlines with different productivity, the right parts show a trend to levelling.The contribution of the NOG and SOG regional stock groups is much more significant for pink and chum salmon.Commercial catch increase is mostly determined by the SOG regional stock group, especially for chum salmon.
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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 | 0.000 | 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".