To the Question of the Use of Climatic-Oceanological Predictors to Forecast Pacific Salmon Stock Abundance in Kamchatka
Bibliographic record
Abstract
During almost 100 years of fishing, an average annual catch of salmon in the Russian Far East (RFE) was about 170,000 tons, of which, approximately 110,000 tons (data for 1971-2020) were contributed by the Kamchatka stocks (Fig. On average, annual RFE salmon catches consisted of 60-70% pink (Oncorhynchusgorbuscha), 25% chum (O. keta), and 10% sockeye (O. nerka) salmon. The other Pacific salmon species together contributed < 5% of the total. The share of chum catches in Kamchatka were slightly lower (up to 15%), while sockeye salmon catches were higher (more than 15%). In the recent decade (2011-2020), annual salmon catches in Kamchatka were generally higher than 252,000 tons.The lowest catch (~ 138,000 tons) was recorded in 2013 and the highest (~ 498,000 tons) in 2018. This appears to be a historic peak of Pacific salmon abundance in Kamchatka in the 20 th and beginning of 21 st centuries
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".