DETERMINATION OF WATER QUALITY STATUS THROUGH ASSESSMENT OF PHYSICOCHEMICAL PARAMETERS ALONG SELECTED LOCATIONS OF ARDA RIVER, BULGARIA
Bibliographic record
Abstract
The physicochemical properties of river water are a key factor for assessing the quality state of a given aquatic ecosystem. Nowadays, the pollution of surface waters, including river systems is mostly related to the anthropogenic pressure exerted directly on them, or indirectly on the catchment area. The main goal of this article is to determine the water quality of the Arda River, the main tributary of the Maritsa River, Bulgaria, in a spatial and temporal aspect, through analysis and assessment of the physicochemical state of the river waters. Water samples were analysed for the period 2015–2023 at four points along the Arda River and 10 physicochemical quality parameters were analysed. In the article, a Canadian complex index for water quality assessment, comparative and graphical methods were applied. The study area was visualised using geographic information systems (GIS). According to the obtained results, the surface waters of the Arda river generally “maintain” a satisfactory quality status according to the norms. The most frequent are the excesses of the values registered above the norms (up to 10 times) for the physicochemical indicators nitrates (N–NO2) and total N. Exceeded reference values for orthophosphates (P–PO4), total N and total P, pH are less common. In addition, the obtained results can be used both in the preparation of specific policies for sustainable management and use of river waters in the Arda river basin, as well as serve as a good basis for further research.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".