Assessment of the Water Quality of the Tigris River in the City of Mosul
Bibliographic record
Abstract
In this study, a total of 120 water samples were collected from five stations along the Tigris River within Mosul City and tested to assess their quality. Water samples were collected over the course of a year. The study included determining thirteen physical, chemical, and biological parameters of the water. The water quality assessment for river water was done using the Weighted Arithmetic Water Quality Index Method (WA WQI), the Canadian Council of Ministry of Environment (CCME WQI), and the method developed by Erdenebayar. The test results showed that the parameters included in the study were within the allowable limits. The overall results of the water quality index revealed that the water quality can be classified as good, which means that the water still meets the different domestic, industrial, aquatic life, and agricultural needs. The maximum water quality index values were 36, 81, and 79, and the minimum values were 18, 76, and 35 for the WA WQI, CCME WQI, and E WQI methods, respectively. A slight difference in water quality was detected during the different seasons, with a slight deterioration in its quality as water flows from north to south. Generally, a good agreement was observed among the three methods.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".