Using The Canadian Method to Classify Irrigation Water Quality Index for Shatt Al- Kufa River Section (Al–Zerkh to Al–Qadisiya)
Bibliographic record
Abstract
Shatt Al-Kufa ( Kufa River) is the main supply of surface waters in Najaf, it is necessary to concern about the river water quality because of increasing demand for different using. This study aims to find the Water Quality Index ( WQI) for irrigation use at many locations on Shatt Al-Kufa within the study section of the river. Nine water quality parameters: Sulfate (SO4 -2), Chlorides ( Cl-1), Total Dissolved Solid (TDS), Electrical Conductivity (EC), Hydrogen Power ( pH), Calcium (Ca+2 ), Magnesium ( Mg+2 ), Sodium (Na+ ), and Potassium ( K+) were studied over ten months ( January- October 2014) for nine locations along the study section. Sodium Adsorption Ratio ( SAR) and Soluble Sodium Percentage ( SSP) were also calculated. Firstly Food Agricultural Organization ( FAO, 1985) was adopted to evaluate the suitability of water for irrigation use. Then Canadian Water Quality Index ( CWQI ) was applied to classify the water quality index of the river for irrigation. It is found that the mean values of parameters for all selected locations are within the highest allowable limits of FAO classification for irrigation, with respect to the values of ( SAR ) the results showed that the water samples were within the level ( S1 ) which there was no harmful effects from sodium, while ( SSP ) values ( except one value ) fall within the water class of good according to Todd classification of irrigation water based on SSP. Results of the annual and seasonal average water quality indices according to the Canadian method were classified as Fair ( 65 – 79 ) in all selected locations. The effective reason which decrease the water quality index, was the presence of high values of ( EC ) and high concentrations of ( TDS ) in all locations, which it refers to high concentrations of salts. Approximately 100 % of EC and TDS water samples have concentrations that exceeded the permissible limits for irrigation water.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| 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) | 0.003 | 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".