A Comparative Study of Water Quality Indices for Karun River
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 it