«Зелёная» экономика в сельском хозяйстве Российской Федерации
Bibliographic record
Abstract
For compliance with international standards, Russian agriculture needs to be updated. An alternative used in agriculture models of governance, may be the introduction of principles of economy. The need to transition to a due to the strong dependence of all sectors from resource extraction industries and technologies. The main carbohydrate media are oil, coal and gas. Today in the world only a few States are engaged in the export of all three types of energy Russia, Kazakhstan, Norway and Canada. There is a paradoxical situation Russia has one of the largest areas designated for agricultural purposes in the world, but the full potential is not used, and the fertile area was reduced. This shows he is the inefficiency of the old resource model. The concept of is based on the understanding that the production depends on the environment and therefore all directions are focused on economical use of natural resources and their conservation. The article presents the concept of economy, the main directions of the concept and experiences of their incarnations in other countries. The industrial model of agriculture based on the use of mechanical production, can not be called perfect, because it is wasteful and makes high demands to get the product. However, the traditional model of manual labour can not cover the basic regional food requirements. Main priorities of development of the directions of green economy in agriculture in Russia, based on international experience, which can increase the competitiveness of the agro-industrial complex for investors.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".