OPTIMIZATION OF THE MAIN DIRECTIONS OF THE DEVELOPMENT OF ANIMAL HUSBANDRY IN KAZAKHSTAN BY THE BENCHMARKING METHOD
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Animal husbandry has always been considered one of the main directions in the agricultural sector of Kazakhstan, being an integral element of the strategic food security of the state, providing employment and income generation for the population. One of the main indicators characterizing the well-being of the country is the consumption of livestock products per capita. Therefore, it is necessary to pay due attention to the qualitative development of agriculture, in particular the development of animal husbandry and increasing its efficiency. Kazakhstan has great opportunities for the development of animal husbandry, this is due, first of all, to the country's natural competitive advantages, such as: favorable natural and climatic conditions, the availability of pastures, as well as the proximity of markets. The purpose of the research was to optimize the main directions of development of animal husbandry in Kazakhstan using the benchmarking method. The processing of scientific and statistical material was carried out by the method of comparative analysis using benchmarking elements. The task of the research included the study of technical, economic and natural and climatic indicators characterizing the level of development of animal husbandry in the countries closest to Kazakhstan in terms of resource provision and climatic conditions. The article presents a comparative analysis of countries such as Mongolia and Canada, it should be noted that the selected objects of comparison are similar in terms of the specifics of the introduction of animal husbandry in Kazakhstan. Such an approach to solving the problem associated with the further development of animal husbandry based on benchmarking contains a certain element of novelty. When calculating planned indicators, normative, calculation-analytical and optimization methods were used.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it