State information system “AITS”: features of formation and directions of development
Bibliographic record
Abstract
Animal breeding is a strategic branch of agri-business in the Republic of Belarus, focused on solving social and economic issues and ensuring national food security. Currently, its development is innovations based, forming a high level of the country’s production and export potential. In the world ranking by the end of 2020, the Republic of Belarus took the 5th place in terms of milk exports (4.8 million tons). As world experience shows, the key direction is implementation of electronic identification of animals as an accounting system in agriculture, including assigning identification number to an animal by tagging, registering information about it in a database and issuing an appropriate passport. The paper summarizes and systematizes legal, organizational and financial conditions for creation and functioning of national information systems in Australia, Argentina, Brazil, Great Britain, the European Union, Canada, the Netherlands, the USA, Switzerland, and Japan. It has been determined that absence of a system for identifying the registration of farm animals leads to distortion of data on the number of livestock, and also creates obstacles for selection work and livestock breeding, decreases efficiency of antiepizootic measures, and inhibits international trade in animals and animal products. In development of this, the necessity of this process in the Republic of Belarus, feasibility of creating the state information system “AITS” (SIS AITS) and corresponding management structure – SI “Center for information systems in animal husbandry” are substantiated. With Gomel region as an example, peculiarities of planning and implementation of process of identification and registration of farm animals are disclosed, the main indicators of the efficiency of SIS AITS for 2013–2020 are analyzed. The advantages of commodity producers of the Republic of Belarus in the context of strengthening production and marketing and export potential and reducing risks in the domestic and foreign markets are substantiated. The issues presented in the article are of interest in determining measures for implementation of the State Program “Agrarian Business” for 2021-2025 and a strategy for the export of agricultural products and food products for the period up to 2025.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".