ADVANTAGES IN GLOBAL ECONOMY OF RUSSIAN AGRICULTURAL PRODUCTION OF GRAIN CROPS
Bibliographic record
Abstract
The Russian Federation is a leading player in the global community, taking the main paths to its formation. The main trend of global economy is globalization, and domestic economies are integrating into the modern system. The Russian Federation is one of the largest countries in the world, located in a variety of climatic zones, and a particularly favorable climate for the development of agricultural sector is in the south. In Russia, 10 % of the world's arable land is located, so more than 80% of the arable land of the Russian Federation is in the Central Volga region, the North Caucasus, the Urals and Western Siberia. Also in the south of Russia melon farming is widespread. The northern regions of the Russian Federation are also subject to successful development with the help of effective agricultural organizations, according to domestic experience, as well as the previous experience of countries such as Finland, Sweden, and Canada, their agriculture mainly operates in similar conditions as the northern and central RF. In October 2014, the Government of the Russian Federation approved a roadmap for import substitution in the agricultural sector for 2016-2017. According to it, the State Program for Agricultural Development for 2013-2020 and the newest prerogative vectors for the development of agro-industrial complex were established and the required resource provision in the amount of 568.3 billion rubles was allocated for 2015-2020, which will help to reduce imports by 1.4 trillion. rub. The ability to enter the world market can be considered as one of the motives for domestic producers of agricultural products and foodstuffs to increase production volumes and measures of state self-sufficiency in agricultural products.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".