SCIENTIFIC AND PRACTICAL JUSTIFICATION OF THE SHORT-TERM LEASE OF AGRICULTURAL MACHINERY
Bibliographic record
Abstract
He paper presents a scientific and practical justification of the possibilities of short-term lease (rental) of agricultural machinery. (Research purpose) The research purpose is analyzing the provision of agricultural machinery to the agro-industrial complex and to reveal the essence of the problem of organizing a mechanism for short-term rental of agricultural machinery. (Materials and methods) Indicated that the technical equipment of the agro-industrial complex remains at the level of 60-65 percent of the regulatory requirement to date. According to the Department of Crop Production, Mechanization, Chemicalization and Plant Protection of the Ministry of Agriculture of the Russian Federation, as of January 1, 2020, the need to purchase only energy-saturated agricultural machinery is: tractors 70 thousand, combine harvesters 38 thousand, forage harvesters 3 thousand. The use of the mechanism of short-term lease of agricultural machinery will improve the provision of equipment, especially during periods of intense agricultural work (sowing, harvesting). (Results and discussion) The analysis and calculations of economic efficiency have shown the possibility of using this type of replenishment of the machine and tractor fleet for all categories of agricultural producers. It will be of interest in terms of financial costs (rent), tax and depreciation benefits during the operation of short-term lease of agricultural machinery. (Conclusions) As a result of the conducted research, it was concluded that the organization of short-term rental (rental) of agricultural machinery in the agro-industrial complex of Russia, especially in the face of large-scale economic sanctions from the EC countries, the USA and Canada, will serve as an effective measure to ensure the machine and tractor fleet.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".