ОРГАНИЧЕСКОЕ СЕЛЬСКОЕ ХОЗЯЙСТВО: РЕАЛИИ И ПЕРСПЕКТИВЫ В КАЗАХСТАНЕ
Bibliographic record
Abstract
Green economy is one of the most important aspects of the country's sustainable economic development. The transition to green economy will allow Kazakhstan to achieve the set goal of becoming one of the thirty most developed countries in the world. The authors analyzed the Concept of green economy, one of its priority directions is the development of organic farming, which is currently a modern global trend. The size of the land areas allocated for the cultivation of organic products in the republic and regions of the world for a number of years is shown and comparative analysis is conducted. The number of Kazakhstani producers of environmentally friendly products and also in other countries of the world is presented. It was revealed that the leading States in this area are the USA, Germany, France and Canada. The organic market there has a pronounced export character. In countries where the organic sector is developed, the farms are members of environmental unions, and undergo certification and food labeling. Certification is not limited to quality control of goods and includes monitoring of land and the entire production process. Kazakhstan is creating a system of integrity and traceability of products of organic origin, contributing to the improvement of ecological balance, conservation of natural resources, maintenance of biodiversity, formation of a national brand with an emphasis on environmental friendliness, taking into account the increase in exports of domestic food products to other countries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".