Russian aquaculture in 2025: growth drivers (a brief overview)
Bibliographic record
Abstract
The Russian Federation’s aquaculture industry has been developing rapidly over the past decade. However, experts note a gradual slowdown in the growth rate of fish production in the country. This may be dictated by several reasons: lack of logical legislation, market imbalance (consumers focus only on cheap or very expensive products), consumer distrust of non-traditional fish species (tilapia, African catfish, cichlids, etc.), lack of advertising content, etc. Nevertheless, according to Rosrybolovstvo, the volume of aquaculture production has more than doubled compared to 2015. The largest amount of products is grown in three macro-regions of our country: the Far East, the North-West and the Southern Federal District. Each district has its own development trends. In the 1st quarter of 2025, Russian aquaculture enterprises demonstrated growth ompared to the same period last year. According to Oleg Lityakin, an analyst at the Rosselkhoznadzor’s Center for Industry Expertise, the volume of production of commercial fish and planting stock in 2025 may reach 389.1 thousand tons. In 2025, the Ministry of Agriculture of the Russian Federation took a significant step in supporting aquaculture by introducing new subsidy measures for fish hatcheries. According to the order of the Ministry of Agriculture dated May 19, 2025, cost limits have been set for subsidizing the costs of creating and modernizing fish hatcheries.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".