The Use of the Leopold Matrix in Carrying Out the EIA for Wind Farms in Serbia
Bibliographic record
Abstract
Although the Energy Sector Development Strategy of the Republic of Serbia until 2015 envisages an increasing share of renewable energy (RE) in the total energy balance of the country and although there are plenty of wind energy initiatives resulting in the elaboration of planning and project documentation, no wind farms have yet been built in Serbia. One of the significant problems in the realization of these projects is the unjustifiable resistance of a part of local authorities and population, particularly related to the possible environmental impacts of the planned projects. In this context, the paper addresses the possibility of using the Leopold matrix in carrying out the Environmental Impact Assessment (EIA) for “Kladovo” Wind Farm in Serbia (case study). The Leopold matrix is a framework method for assessing the environmental impact of a project. The novelty in this method, presented herein, refers to the evaluation of planned project activities relative to a group of criteria related to: significance (spatial dispersion), probability and duration of impact. The obtained EIA results have enabled the precise identification of possible environmental impacts of “Kladovo” Wind Farm project, as well as removal of dilemmas and problems related to the public resistance to the realization of the project through a transparent relationship with stakeholders.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".