DIGITAL TECHNOLOGY DEVELOPMENT TRENDS IN AGRICULTURE
Bibliographic record
Abstract
Ensuring sustainable rates of Russian agriculture development requires significant investment, which is due to the introduction of digital technologies in the industry. The solution to this issue in practice is impossible without the implementation of federal projects and programs by the state. In recent years, certain trends in increasing production volumes have been achieved in the agricultural sector of Russia, which is also positively influenced by the digital solutions introduced into agriculture. Technologies with a quick payback have become especially popular among farmers of our country: fuel and lubricant consumption monitoring sensors; cloud solutions, the main purpose of which is forecasting, accounting, planning; digital solutions for agricultural enterprises with an industrial type of production; and the least - technologies related to forecasting or modeling due to their longer payback. By 2022, there is a decrease in the gap in the indicators characterizing the availability of websites, use of internet, computer networks in agricultural formations and the average values for all organizations in the country. The development and implementation of digital technologies in agricultural production requires the use of an integrated approach that would include the interaction of the state and business in the industry in order to achieve higher indicators. At the same time, we must not forget about legal support, and it is also necessary to improve the technological, organizational, financial, personnel components in solving the problem under consideration.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".