Evaluation of Ground Water Quality Status by Using Water Quality Indices at Basheqa Region, Iraq
Bibliographic record
Abstract
Large areas of BASHEQA region haven't any source of surface water, at the same time, there are large quantities of olives trees and crops depend in its irrigating on Ground Water (GW) as a main source. So it is important to evaluate its (GW) for different uses. In this study the (GW) of 32 wells had been examined in the college of environmental science and technology laboratories to assess its Water Quality (WQ) for drinking, irrigation, and livestock purposes. Average twelve parameters (pH, Ca, Mg, Na, HCO3, SO4, Cl, NO3, EC, TDS, SAR, TH) data in the period 2008-2009 had been applied in three methods through computing Water Quality Indices (WQIS). The first method was the Weighted Average (WAV). The second one was that adopted by Ministry of Nature and Environment (MNE) of Mongolia, while the last one was the Canadian Council of Ministers of the Environment (CCME). The (WQIs) of the three methods results had been compared to assess the suitability of the best one. Although the statistical analysis indicated that there are no significant differences between both (CCME) and (WAV) methods, the (WAV) data had been used in this study as it gave more restrictive control. The analysis of (WQIs) using (WAM) method indicated that (25, 69, 88)% of (GW) are good for drinking, irrigation, and livestock purposes respectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".