To the Question of the Use of Climatic-Oceanological Predictors to Forecast Pacific Salmon Stock Abundance in Kamchatka
Bibliographic record
Abstract
Pacific salmon, forecasting of return abundance, water temperature anomaly, feeding migrations During almost 100 years (1925-2020) 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. 1).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 (Bugaev et al. 2020).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".