The relationship between the price and compensation for change of agricultural land use in the municipality of Litija
Bibliographic record
Abstract
The thesis deals with the sale of agricultural land in terms of ratio between the selling price and the amount of compensation for the change of use during the period of 2011 and the first quarter of 2012. General presentation is followed by a central part of the thesis, which is aimed at analyzing the sales price and damages. The practical part thoroughly analyzes only the relationship between the selling price and the compensation for change of agricultural land usage in the Municipality of Litija. Presented are the essential elements that affect the amount of damages and their dependence on the selling price. More detailed analyses are conducted on the basis of the acquired sample of sold agricultural land in the Municipality of Litija. I assumed that the selling price is dependent on the amount of compensation for the change of agricultural land use. The methodology for calculating the amount of compensation is determined by the Law on Agricultural Land (ZKZ-C, Ur. list 43/2011), which depends on the credit rating and the size of agricultural land. In theory, the land with a higher rating must be sold at a higher selling price, precisely because of higher compensation in the event of conversion of agricultural land. The analysis showed that the selling price does not depend on the amount of compensation.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".