Evaluation of Water Quality in a Highly Impacted Urban Stream Using Water Quality Index (Ankara Stream, Türkiye)
Bibliographic record
Abstract
Population growth along with other factors such as industrial, agricultural, and urban development, threaten freshwater resources in urban areas. Protecting urban water quality for ecological balance, water security, and energy production is crucial. The water quality index (WQI) provides an effective tool for assessing and managing water quality, and the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI) is one of the extensively used method. In this study, the pollution status of the Ankara Stream which flows through the densely populated Ankara was examined using physico-chemical parameters collected from five stations (S1-S5), and the water quality status was estimated via CCME-WQI. The results revealed varying water quality across different points on the stream. S2, located in a protected area, exhibited the best quality; in contrast, S4 and S5, located downstream of a wastewater treatment plant, exhibited the poorest quality. The consistency of these findings with the literature and the historical records of Ankara Stream emphasize that the CCME-WQI can be used for the management of water resources with high levels of pollution. This study contributes to sustainable water management practices and highlights the need for advanced treatment techniques to control pollution in urban freshwater resources.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".