Evaluation of Bashar River water quality using CCME water quality index
Bibliographic record
Abstract
Water quality index is an essential part of the water resources management system that is used as a numerical scale for evaluating and classifying water quality for various uses. The present study focuses on the application of the Canadian Water Quality Index (CCME WQI) to assess the water quality of the Bashar River for drinking, agricultural, aquaculture, recreational and recreational purposes, livestock, as well as the use of the Schuler diagram to assess the quality of drinking water in this river. In this study, 3 sampling sites along Bashar River and in different seasons of 1398, 4 samples were taken from each site and examined. The results showed that Bashar's river's water quality for drinking is high and medium, and in terms of aquaculture and recreation and recreation is high and good in terms of agriculture and livestock consumption. Also analysis of Schuler plots showed that the quality of the water of Bashar River in terms of drinking is in the good and medium range.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 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".