Assessment of Multi-Depth Water Quality Dynamics in an Artificial Lake: A Case Study of the Ribnica Reservoir in Serbia
Bibliographic record
Abstract
High water quality in reservoirs used for drinking water supply and located within protected areas is of crucial importance for sustainable water-resource management. This study aims to evaluate the multi-depth water quality dynamics of the Ribnica Reservoir in western Serbia, combining two standardized assessment tools: the Serbian Water Quality Index (SWQI) and the Canadian Water Quality Index (CWQI). Data collected at various depths during 2021 and 2022 were analyzed to assess physico-chemical parameters and their impact on water quality, while the absence of microbiological data was noted as a limitation affecting the comprehensiveness of the assessment. The SWQI results indicated a general improvement in water quality over time, with values ranging from medium (82) to excellent (95) in 2021 and increasing from good (89) to excellent (98) in 2022. In contrast, the CWQI revealed specific risks, notably elevated concentrations of aluminum, mercury, and chromium, and reduced dissolved oxygen levels, with overall CWQI values ranging from poor (40) to good (88) depending on depth and parameter variability. The study highlights the necessity for continuous, comprehensive monitoring, including microbiological analyses and seasonal assessments, both within the reservoir and in the Crni Rzav River and its tributaries, to better understand pollutant sources and catchment influences. Strengthening microbiological and heavy metal monitoring, along with implementing proactive management strategies, is essential for preserving the Ribnica Reservoir’s ecological integrity and securing its long-term role in drinking water provision.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".