Financial Impact of Digital Technologies as a Promising Element of Import Substitution
Bibliographic record
Abstract
This article examines the financial elements of agricultural production digital architecture in Russia during the transition to the fourth technological stage in order to compete and import substitution in the agro-industrial market of the country. The pandemic and its consequences have had a negative impact on the Russian economy, in the context of the country's insufficient food security and the inevitable increase in prices for imported goods, due to rising prices for hydrocarbons and the predominance of low-value-added food products in exports. Due to the weakening of the ruble against the falling oil prices, the economy may face the need for a new wave of import substitution. This study identifies the problems of the agricultural sector and the reasons for Russia’s high dependence on imports justify the need to develop high-tech technologies. Authors determined the development directions of agricultural production digitalization in Russia through innovative agricultural technologies based on the Internet of things, distributed computing, and artificial intelligence technologies. Increasing the level of the agro-industrial complex development, bringing it to a new independent stage, is vital. However, it is impossible without state support and the digitalization of technological processes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".