Application of CCME water quality index for drinking purpose in Tigris River within Wasit Province, Iraq
Bibliographic record
Abstract
The water quality index (WQI) is an essential part of the water resource management system through its use as a numerical scale to evaluate and classify the quality of the water body for various beneficial uses (drinks, industry and irrigation).The present study used WQI based on the Canadian Council of Ministers of the Environment (CCME-WQI) as a tool for assessing the quality of Tigris River in Wasit Province, Iraq for drinking purposes through assaying different chemical and physical parameters. Four sites were selected along the river between Al-Kut City and Al-Aziziyah from August 2016 through July 2017. Water samples were collected monthly and nine physicochemical parameters were selected: pH, dissolved oxygen, nitrate, phosphate, sulfate, chlorides, lead, zinc and manganese. Based on the results of water quality index, the river water was considered as marginal in all the studied sites, and the CCME-WQI ranged between 56-62. The highest deviation has been occurred in phosphate, nitrate, sulfate, lead, and manganese, leading to decrease the water quality index value.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".