The Analysis of Water Quality Using Canadian Water Quality Index: Green Belt Project/Kerbala-Iraq
Bibliographic record
Abstract
During the last five decades, a huge amount of water pollutants has been recorded in all water resources around the world. Therefore, the water quality has become an important indicator affecting the vitality and productivity of plants, which requires an effective technique to monitor all these pollutants. The main objective of this study is to assess the validity of groundwater for wells located within the boundaries of the Green Belt area in Karbala city/Iraq for irrigation of palm and olive trees. Whereas, the use of saline groundwater as an alternative to available fresh water will promote the sustainable development of water resources. The technique of Water Quality Index (WQI) is a reliable and widely used tool for assessing water quality for various sources, including groundwater. In this study, the Canadian water quality index (CWQI) model was applied to provide a database for planning and monitoring the quality of groundwater in wells located in the study area. Groundwater samples were taken from these wells and tested to find seven parameters which were; pH, CL, Mg, HCO3, EC, Na and Ca. The CWQI values of groundwater for the studied wells ranged from 30 to 35. According to the CWQI scale, the groundwater of all wells is classified as poor water. Therefore, the groundwater of all wells in the study area must be treated before it is used for the purpose of irrigation of palm and olive trees. This study concluded that to ensure good irrigation management in the study area, future changes of groundwater in the study area must be monitored.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".