A Comparison between Weighted Arithmetic and Canadian Methods for the Drinking Water Quality Index, Al-Abbasia River, Najaf, Iraq
Bibliographic record
Abstract
In this study, eight samples were analyzed along Al-Abbasia River, where the Water Quality Index and pollution or changes in water quality were studied. The Water Quality Index is a useful and rapid technique for evaluating the quality of any water source. The samples were taken along Al-Abbasia River and on the area basis which influence the river. The physical and chemical parameters (pH, Turbidity, Alkalinity, Electrical Conductivity, Total Disollved Solids, Total Hardness, Ca2+, Total Suspended Solids, Na+, Cl-, K+, SO42-, Mg2+) were evaluated in this study using the Weighted Arithmetic Water Quality Index and Canadian Council of Ministers of the Environment Water Quality Index methods which shows the extent of pollution. According to Weighted Arithmetic Water Quality Index, the water quality is classified as poor except for the second sample, where it was classified as Very poor (according to the standards of the WHO), while according to the Canadian Council of Ministers of the Environment Water Quality Index method, the water of Al-Abbasia river was often classified as a Good, except for the second and forth samples they were classified Fair. This study showed that the main cause of the deterioration of water quality in Al-Abbasia River is the direct discharge of sewage, human activity, and harmful residues of pesticides and materials which used in agriculture.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| 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) | 0.001 | 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".