پایش کیفیت آب رودخانههای استان خوزستان جهت مصارف شرب، صنعت و کشاورزی با استفاده از شاخصهای IRWQIsc و NSFWQI
Bibliographic record
Abstract
Background and Aim: Monitoring water quality is so important so as to decide about using them. So, this research was conducted to evaluate Khuzitan’s river water quality. Materials and Methods: The rivers were studied consist of Dez, Karkheh, Maroon, Karoon and Zohreh. Data collecting was applied during 2018 for each river from specified stations. So, water quality standard of Iran, WHO and Canadian council of ministers of the environment, and Shoeller diagram and Wilcox diagram were used. In addition, IRWQIsc and NSFWQI standards were used to categorize river water quality. Results: The results showed that Dez water was industrially corrosive, while other rivers had sedimentary water for industrial use. The water quality of Dez was better than other rivers in Khuzestan province, but this river also had high magnesium, hardness and chlorine based on the Shoeller diagram. The quality of this river was better for agricultural purposes rather than the others. Karun River was moderately better than other rivers, and water quality is better upstream than downstream. According to IRWQIsc index, the water quality variations of Dez, Karkheh, Karoon, Maroon and Zohreh were 71-83, 41-52, 39-55, 33-41 and 25-32, respectively. The results of NSFWQI index for Dez, Karkheh, Karoon, Maroon and Zohreh rivers showed that the values of these rivers varied between 65-77, 55-70, 58-68, 52-60 and 36-48, respectively. Conclusion: Thus Dez River was in relatively good condition. Karoon and Karkheh rivers were in moderate condition and other rivers were in relatively poor condition. According to all indices, water quality of Zohreh River was in poor condition and Dez River was in good condition. Other rivers had medium quality.
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How this classification was reachedexpand
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.211 | 0.004 |
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; both teacher heads agree on what is shown here.
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".