Water Quality Assessment of Aflaj in the Mountains of Oman
Bibliographic record
Abstract
The research was conducted to assess the aflaj water quality in Al Jabal Al Akhdar, Oman. 9 aflaj were sampled during summer and winter seasons in 2012-2013 to evaluate for the physico-chemical characteristics of major quality parameters; and assess the suitability of aflaj for irrigation purposes. Samples collection, handling and processing followed the standard methods recommended by the American Public Health Association and analysed in quality assured laboratories using appropriate analytical methods and instrumental techniques. The quality parameters of the selected aflaj water indicated their suitability for irrigation as most of the quality parameters were within the permissible limits set by Omani regulations of wastewater reuse for irrigation. These selected water resources are excellent or good in quality for irrigation purposes based on the evaluation of different hazards parameters including the salinity-alkalinity hazards which indicate good to admissible water based on electrical conductivity and sodium adsorption classification; and water quality indices which reveal high or moderate classes, indicating the suitability of aflaj for irrigation of the majority of crops and soils. This study is a first comprehensive assessment towards providing indicators and classification indices on irrigation water quality of this fragile mountain ecosystem, which will be the basis for future planning decisions on agricultural demand management measures to protect these principal resources for agricultural production in Al Jabal Al Akhdar.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".