TRENDS IN THE FORMATION AND CORRELATION OF CURRENT AND NON-CURRENT ASSETS OF AGRICULTURAL ENTERPRISES: A CASE STUDY OF UKRAINE
Bibliographic record
Abstract
The study aims to identify trends in the formation of the structure of assets of agricultural enterprises in Ukraine and the ratio of their individual groups. In preparation of work the complex methods of economic research used in this study were: monographic, critical analysis, structural and trend analysis, correlation-regression analysis, etc. The study found that in the current economic conditions, the technical potential and repair and maintenance base of the agricultural sector of Ukraine does not meet the requirements of scientifically sound needs of agricultural production. The supply of machinery to most agricultural producers in Ukraine is approaching a critically insufficient level. It is substantiated that the main agricultural machines of agricultural enterprises of Ukraine are provided only by 45-65%. The article proves that more than 90% of the technical means of agricultural enterprises of Ukraine have already served their depreciation period; their technical readiness for fieldwork does not exceed 60-70%. The article substantiates that due to malfunctions and physical wear and tear, a quarter of tractors and combines are not used in Ukraine every year, and the technical service system operates at minimum capacity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".