Quality assessment of water quality in Iraqi cities
Bibliographic record
Abstract
The quality of drinking water directly affects human health and life. This study is concerned with the assessment of the drinking water quality in the main cities of 14 Iraqi governorates for the years 2011 and 2017 based on the Iraqi Central Statistical Organization statistical data for nine parameters. The Canadian method was used to calculate the Water Quality Index. The results showed a slight difference with the preference for water quality for the year 2011. The water quality is ranked excellent depending on the minimum values of statistical data, good based on the average value of statistical data, and poor depending on the maximum values of statistical data. The most influential parameters for the deviation of the water quality index values are sulphate and turbidity, and the lowest are the pH and electrical conductivity. The study showed that the water quality index does not give a consistent trend of the water quality change during the years 2011 and 2017. Therefore, the study recommends the use of other advanced statistical methods for this purpose. The study showed that the statistical data give less accurate results than the results of the tests, but it gives a clear initial picture for the decision-makers when the study area is wide and the research team is not available and the time period is long.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".