Groundwater Quality and Sustainability Evaluation for Irrigation Purposes: A Case Study in an Arid Region, Iraq
Bibliographic record
Abstract
Water resources are of great importance in the world's agriculture, especially regarding the scarcity of these resources. That calls for attention appropriate for analysis, study, and research in all issues and aspects that would contribute to the development and maintenance of those resources and achieve the maximum possible levels of quality and efficiency of use. The study was conducted during the fall season of 2020 to study groundwater quality (well water) in the city of Al-Qaim of Anbar Province to explain its suitability for agricultural exploitation in the region. The study included seven sites in Al-Qaim (Rtemi, Medicis, Eastern Akash, Okesha, Sawab, Albu-Hayat, and Al-Karah) to assess the validity of irrigation. The pH, Electrical conductivity (EC), positive ions (K+, Na+, Mg2+, Ca2+), and negative ions (HCO3, Cl-2, SO4) and CO3 were measured. The results showed that the studied well water is acceptable for irrigation purposes in terms of pH value, as for the electrical connection, it was six wells within the class (C2), which is adequate for irrigation for medium-salty crops, except for one well, which is a fine well within class C3 suitable for irrigation of high salinity crops. The total hardness values were low and did not pose any risk; as for the positive and negative ions, they were within the permissible limits within the specified classifications. By measuring the positive ions, the Sodium Adsorption Ratio (SAR) value was calculated and determined to be within the class S1, which means that the groundwater in the region is suitable for irrigation.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".