Integrating multivariate statistical analysis and canadian water quality index to assess seasonal variation in Duhok Dam, Iraq
Bibliographic record
Abstract
The escalating demand for freshwater resources in arid and semi-arid regions necessitates comprehensive assessments of water quality to ensure sustainable use for domestic and agricultural purposes. This study evaluated the seasonal water quality of Duhok Lake by integrating multivariate statistical techniques Principal Component Analysis (PCA) and exploratory Cluster Analysis (CA) with the Canadian Water Quality Index (CCME WQI). PCA identified five principal components explaining 92.5% of the total variance, revealing the dominant influences of domestic discharge, eutrophication driven by phosphate and phytoplankton proliferation, geological contributions, agricultural runoff, and mineral ion enrichment. CA was applied in an exploratory sense only, to visualize similarity patterns among sites, seasons, and variables. The CCME WQI classified Duhok Lake’s water as marginal for both drinking (55.6) and irrigation (58.5), primarily due to elevated levels of sulfate, hardness, total dissolved solids, and magnesium exceeding WHO standards. Historical comparison over two decades revealed fluctuating trends linked to variable inflows and precipitation. These findings highlight the urgent need for continuous monitoring and integrated management strategies to mitigate emerging risks and ensure the sustainable use of Duhok Lake as a vital freshwater resource.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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".