FOOD COMPLEX OF RUSSIA: ANALYTICAL REVIEW, RISKS AND THREATS, PRIORITIES AND PROSPECTS
Bibliographic record
Abstract
Currently, Russia is facing unprecedented sanctions restrictions (at the beginning of 2023, more than 10.5 thousand), as well as military-political resistance from more than 50 developed countries of the world, led by the United States, EU member states, Canada, Japan and Australia. In this tense situation, it is important to maintain socio-economic, social and political stability. One of the key factors for the successful implementation of this strategic setting is the effective functioning of the country's food complex in the context of ensuring the necessary level of food, economic and national security. Russia has a high potential for the production of agricultural products, raw materials and food (natural and climatic, material and technological, research, innovation and investment, transport and logistics, personnel, organizational and managerial, regulatory and legal). About 55% of the world's chernozem, 1/5 of fresh water reserves are concentrated in our country, there are extensive forest areas that contain the negative effects of climate change, in addition, today Russia produces about 25 million tons (in active substance) of mineral fertilizers. Thus, the research topic is relevant and has a high level of perspective and utilitarian significance. The paper assessed the current state of the food supply of our country, noted the features and problematic aspects of the domestic agricultural market, also proposed promising solutions and highlighted strategic guidelines for the development of the domestic food complex of Russia.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| 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) | 0.002 | 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".