DIGITAL ECONOMY TECHNOLOGIES IN THE AGROTECHNOLOGICAL MODEL
Bibliographic record
Abstract
The article examines the application of modern technologies, in particular blockchain, artificial intelligence (AI) and the Internet of Things (IoT), in agriculture. The authors emphasize the importance of digitalization of the agricultural sector to improve efficiency, sustainability and food security. The article analyzes the advantages and disadvantages of blockchain technology, such as security, cost reduction, speed and versatility, as well as uncertain regulatory status, high energy dependence and scalability. Examples of blockchain use in the agricultural sector are described, for example, the Grain Discovery platform in Canada, to improve the transparency and efficiency of supply chains. Then, the possibilities of AI in agriculture are considered, including plant disease identification, weed classification, water management, weather and crop yield forecasting. The authors distinguish four types of AI and note the importance of standardization and regulation of this technology. A SWOT analysis of AI application in the agricultural sector is provided, where the strengths are increased productivity and efficiency of management decisions, and the weaknesses are the need for significant investments and the length of time it takes for technologies to enter the market. The final part of the article discusses the Internet of Things (IoT) technology, its components and application in agriculture. The authors provide statistics on the growth of the IoT market in Russia and note the importance of developing national standards in this area. Examples of IoT use in agriculture are described, such as GPS trackers, animal activity sensors, precision farming systems and RFID technologies. The authors conclude that agricultural technologies are a key tool for the development and transformation of agriculture.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".