Assessment of Lake Water Quality in Central Serbia—Using Serbian and Canadian Water Quality Indices on the Example of the Garaši Reservoir
Bibliographic record
Abstract
The water quality in lakes and reservoirs is crucial for maintaining ecological balance and ensuring public health. This research focuses on the water quality evaluation of Garaši Reservoir in Serbia, a vital source of drinking water for surrounding communities. We systematically analyzed three profiles (A1, B1, and C1) at various depths ranging from 50 cm to 1500 cm between 2021 and 2023. The study employed the Serbian Water Quality Index (SWQI) and the Canadian Water Quality Index (CWQI) to evaluate the water quality. The findings revealed significant spatial and depth-dependent differences. Higher concentrations of Aluminum (Al), Mercury (Hg) and Manganese (Mn), influenced by the inflow from the Velika Bukulja River, resulted in reduced overall water quality and suitability for drinking water. Dissolved Oxygen levels decreased with depth, indicating thermal stratification and nearly anoxic conditions, which are harmful to aquatic life. Some shallow areas exhibited poor water quality for recreational use due to high pH and metal concentrations. The study underscores the necessity of continuous and comprehensive monitoring to identify pollution sources and implement mitigation measures. Such efforts are essential to protect biodiversity and ensure the sustainable management of water resources in lakes and reservoirs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".