Quantifying Canadian Water Quality Index in Alhindya Barrage, Euphrates River
Bibliographic record
Abstract
Abstract Recently, water quantity is decreasing due to high temperatures in summer and reducing the water quotas of Euphrates River in Iraq from the neighboring countries. These factors may increase turbidity and concentration of elements in Euphrates River especially near Alhindya Barrage, Babylon Governorate, Iraq. Thus, Euphrates River may not be valid for drinking and irrigation purposes in upstream of Alhindya Barrage. The aim of this study was to investigate the water quality upstream of Alhindya Barrage, Euphrates River, using Canadian Water Quality Index (CWQI). The comparison of water quality was investigated in the years of 2008 and 2009 according to data availability. Statistical analysis were performed on measured flowrates and indicated that there is a statistically significant difference between measured flowrates for 2008 and 2009. The results showed that the CWQI of 2008 was 94 which is good to excellent water quality, compared to CWQI of 79 for 2009. This was due to reduction in mean water quantity from 370m3/s of 2008 to 213m3/s of 2009. The global warming phenomenon is the main reason for dry seasons and low rainfall intensity and caused bad water quality.
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 | 0.002 |
| 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".