INTERNATIONAL EXPERIENCE IN AGRICULTURAL LAND PROTECTION THROUGH SPATIAL PLANNING METHODS (REVIEW PAPER)
Bibliographic record
Abstract
While urban land use planning receives much attention in land management in Bulgaria, agricultural land use planning has been significantly neglected. Today, more than ever, effective and efficient land use planning is needed to address the global and local challenges, such as protecting prime agricultural lands for food production, achieving sustainable productivity gains, improving water use efficiency, reducing greenhouse gas emissions, etc. The objective of the paper is to review the international experience in rural and agricultural land use planning and its theoretical foundations, to identify and analyze the gaps in Bulgarian legislation in this area, and to develop some scientific ideas for including agricultural land planning into Bulgarian Regulatory Framework and its further implementation in practice. The need for agricultural territory planning in Bulgaria is emphasized. Intra-zoning of agricultural land on a holistic basis is proposed as a useful planning method for balancing agricultural development with the environmental protection, while maintaining both land productivity and the quality of natural resources. Information from strategic planning documents and literature resources from the USA, Canada, Australia, European and other countries is presented and analyzed. Issues of building permits, land categorization systems and assessment of land suitability for growing agricultural crops are studied.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.017 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".