Evaluation of water quality of Chehel-chai River in northern Iran based on NSFWQI, IRWQIsc and CWQI
Bibliographic record
Abstract
Background and Objective: The increasing development of agricultural and aquaculture activities along the rivers has reduced the quality of running water. The aim of this study was to evaluate Chehel-chai River water quality with national sanitation foundation water quality index (NSFWQI), Iran water quality index for surface water (IRWQISC), Canadian water quality index (CWQI). Methods: This descriptive-analytical study, was performed on all of 7 sampling stations based on standard factors such as availability, land use type, geology and dispersion along the river, 12 water quality parameters including dissolved oxygen, fecal coliform, pH, biochemical oxygen demand (BOD), chemical oxygen demand (COD), temperature, organic phosphate, nitrate, ammonium, turbidity, total soluble solids and electrical conductivity and 5 cations (sodium, calcium and magnesium) and anion (chloride and sulfate) along the river for summer and autumn seasons 2018 (42 sample) with the standard method was measured. Results: The amount of phosphate and turbidity increased from station 2 to downstream due to the existence of fish ponds and agricultural drainage. BOD, COD and fecal coliform values at station 6 have increased significantly with due to urban effluent output. River pollution in the summer has increased due to reduction of river flow and after station 3 (promenade) to the downstream, which is due to the entry of agricultural fertilizers and urban wastewater discharge. According to the average of IRWQISC and NSFWQI, the water quality of Chehel-chai River in the sampling station in the area of Minoodasht city (station 6) is in bad class. The CWQI index showed that the water of the Chehel-chai River is suitable for drinking and aquaculture at the border of the class, for agriculture in the bad class, and in terms of recreation and livestock use in the higher class. Conclusion: The mean values of the above indices indicate high pollution quality class, and since this river is used for water supply for agricultural and aquaculture, management strategies are necessary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".