STRUCTURIZATION OF THE REGIONS OF UKRAINE BY THE INDICATOR OF CASH ESTIMATION OF AGRICULTURAL LANDS
Bibliographic record
Abstract
The article reveals the issue of structuring the regions of Ukraine by indicators of monetary valuation of agricultural land. On the basis of the data of the State Statistics Service of Ukraine the analysis of the structure of agricultural land was carried out, which made it possible to establish a fraction of the area of individual species of land in total. The focus is on the concentration of significant amounts of land in private ownership, which exacerbates the issue of land valuation from the perspective of possible resource management and efficiency in its use. It has been argued that fragmentation of land is one of the good reasons for the inefficient use and changing purpose of land, lack of financial resources and smallholder coherence. In accordance with the Law of Ukraine "On Land Assessment" and the data of directories of the State Service of Ukraine on Geodesy, Cartography and Cadastre for 2017 - 2019. A comparative analysis of the normative monetary valuation of agricultural land by regions of Ukraine was carried out, which became the information base for their structuring by the method of cluster analysis. It was substantiated that one of the powerful methods of multivariate analysis is the cluster analysis, which is based on a set of selected economic indicators and objects of assessment. Estimates are based on the monetary valuation of agricultural land such as: arable land, perennial plantations, hayfields and pastures. On the basis of mathematical standardization of values of indicators the matrix of imaginary Euclidian distances is calculated, became a basis for formation of 7 clusters, each of which includes a final number of objects-regions distributed on homogeneous signs and approximation on estimations of cost of land areas. The formation of clusters on such characteristics will identify the most similar groups of objects-regions to develop a system of monitoring changes in the cost of land resources with subsequent analysis of fluctuations relative to average levels within specific clusters, and in Ukraine as a whole.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".