Water Quality Assessment of Euphrates River Within Fallujah City Using Water Quality Indices Technique
Bibliographic record
Abstract
Scientist and researchers were recognized the important of water quality, Water resources are especially susceptible aquatic systems to many pollutants sources. Determination of water quality characteristics are very significance in term of the environmental considerations. This study aims to assess the water quality of the Euphrates river in the Fallujah Euphrates Reach (FER) by measuring the concentration of chemical and physical properties of water using multiple devices. CWQI and WAWQI were also used to assess the quality of water and indicate its suitability for drinking purposes, based on standard specifications from IQS, WHO, and USEPA as a criterion for comparison purposes. The findings show that with the use of CWQI, the quality of water was fair based on IQS, good based on WHO, and marginal based on USEPA, while the quality ranged between good, poor, very poor, and unsuitable for drinking purposes as a result of using WAWQI, with the exception of station 7, where the value of the WAWQI was greater than 100 mg/L, which indicates that the water is not suitable for drinking purposes as a result of the pollution of this station from a point source, which caused the high concentration of turbidity, sulfate, BOD5, and total dissolved solids, and decreased the DO concentration in this station compared to the rest of the stations, which were within the permissible.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".