Evaluation Water Quality Index for Irrigation in the North of Hilla city by Using the Canadian and Bhargava Methods
Bibliographic record
Abstract
To know water quality for multi uses in the north of Hilla city, it is important to study the quality of the water parallel with the quantity. In this research, two national methods are adopted to evaluate and judge the suitability of Euphrates River in this zone (study case) for irrigation use. These methods are the water quality index (WQI) of the Canadian and Bhargava model. The main river passing through the north of Hilla city is Euphrates River and his branch Hilla River, the uses of its water are different and its use for irrigation depends on many environmental parameters. The researcher studied the quality of this river for irrigation use during 2011. Took four stations on the river in Babylon (Euphrates River/AL-Musiab, Euphrates River/Kifil, Hilla River/ Hindia barrage and Hilla river/Hilla. The main results showed that there is no difference between the two techniques at significance level (0.08) and the quality of the river inter the boarder classified as GOOD and FAIR according to Bhargava and the Canadian method respectively. Also that there is a serious deterioration in the water quality downstream Al-Kifil station because of the local drains that discharge in the river. These results ensure the need to receive higher water quality at the boarders (quantity and quality) to raise the quality in the downstream the river.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".