Sustainable Water Resources Management and Groundwater Quality Assessment: Case of Karbala, Iraq
Bibliographic record
Abstract
Barren lands can be converted into agricultural land through a multidisciplinary approach to water management. This study evaluates the groundwater quality for irrigation in the uncultivated Faddak land (277 km²) north of Kerbela City, Iraq. Thirty groundwater samples were collected from regional wells and analyzed using GIS, testing, and international standards from the FAO and the Canadian Council of Ministers of the Environment (CCME). A range of physicochemical parameters were tested, including calcium, magnesium, sodium, potassium, sulfate, chloride, total dissolved solids, electrical conductivity, and others. The results showed that most pollutants exceeded permissible limits, with an Irrigation Water Quality Index (IWQI) of 36.28, indicating that the groundwater was unsuitable for irrigation. It is concluded that the water demands of native plants can be met through a combination of rainfall and surface water from the Euphrates River, with a maximum release of 20 m³/s required for cotton cultivation in July if 50% of the area is planted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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".