A Comparative Study of Water Quality Indices for Karun River
Bibliographic record
Abstract
Water quality is an important factor for preservation of human life and aquatic ecosystem. In rivers, water quality is affected by the environment, climate condition, seasonal variation, land-use, natural and man-made pollution of watershed. Considering growth of water use for different consumptions and discharge of pollutions in rivers, several water quality parameters are usually monitored along rivers in different periods. However, there is a need to combine results of such measurements in the form of composite indices which are understandable to decision makers and general public. For this purpose, some indices for classification of water quality in rivers have been applied world wide recently. In this paper, two Water Quality Indices (i.e. National Science Foundation of the USA and Council of Ministers of Environment of Canada) were trialed for the case of Karun River system which is the most important river of Iran. These indices were calculated using existing data and their variations have been analyzed and compared in 9 stations, located along the river, for different periods. Results showed that application of these simplified indices was satisfactory for the educational case study and could be replicated for other communities in Iran.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".