Preliminary Assessment of Hydrological Heritage in North Macedonia: A Novel Contribution to Geodiversity Protection
Bibliographic record
Abstract
Abstract This study inventories, classifies, and evaluates the hydrological heritage of North Macedonia, categorizing key features into four main groups: (1) river basins, including rivers and waterfalls; (2) springs, classified as karst and thermal; (3) natural lakes, subdivided into tectonic, glacial, landslide, denudation, and karst types; and (4) marshes. Recent measurements indicate a significant decline in the water levels of glacial lakes in North Macedonia. The deepest glacial lake in North Macedonia is Golemo Pelistersko Ezero Lake (17.2 m). Using geospatial analysis and the Geosite Assessment Model (GAM), this research assesses 10 selected glacial lakes based on their size and hydrological significance, focusing on their vulnerability to climate change and risk of disappearance. The GAM evaluation examines each lake’s Main Values (MV)—scientific, educational, aesthetic, and tourism attributes—as well as Additional Values (AV), including accessibility, infrastructure, and educational resources. The findings reveal significant diversity in the hydrological heritage of North Macedonia’s glacial lakes. Notably, Bogovinsko Ezero Lake achieved the highest Main Value score of 11.5, reflecting its exceptional scientific importance and aesthetic appeal, alongside an Additional Value score of 13.0. Given the increasing risks to these hydrological sites, this study underscores the urgent need for protective measures. Overall, the research enhances the understanding of hydrological heritage in North Macedonia and provides data-driven recommendations for sustainable management and conservation strategies, integrating these water sites into broader geodiversity protection efforts.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".