Reconstructed Russian Fisheries Catches in the Barents Sea: 1950-2014
Bibliographic record
Abstract
The management of marine living resources that straddle country borders has historically been a challenge, particularly in cases where political tensions are high. The jointly managed fisheries resources in the Barents Sea are a notable exception, wherein the Russian Federation (formerly Soviet Union) and Norway have relatively successfully managed fish stocks together since the 1950s, including during the high tensions of the Cold War and the dissolution of the Soviet Union. Using ICES statistics as reported baseline landings, the total catch of the region by the Russian fisheries was reconstructed for the period 1950-2014. Total catch was divided into reported landings, unreported landings, and discards, and assigned to four sectors: industrial, artisanal, recreational, and subsistence. Unreported landings and discards between 1950 and 2014 accounted for approximately 12% and 55% of the total catch, respectively, with discards being substantial in the early decades. A majority of the catch was caught using pelagic and bottom trawls, contributing to the high rate of discards. Both discards and landings reached their peak in the 1970s, after which overexploitation contributed to numerous stock declines. Stocks recovered in the 1990s following adoption of legislation and gear regulations limiting discards as part of a joint effort by Norway and Russia to more sustainably manage stocks. The trend of declining Russian Barents Sea catches after the 1980s matches global trends of declining catch, although the present case appears to be mainly due to more successful management interventions. It is assumed that small-scale fisheries removals are minor in the region, but further research to refine estimates of small-scale fishing can improve upon the present study. While this study highlights historical declines in catch due to overexploitation, it does not explore fluctuations in catch caused by environmental variation. In the rapidly warming Arctic region it is of vital importance to understand how stocks may be further affected by climate change in addition to fishing pressure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".