Monitoring and Assessment of Surface Water Quality Using Physicochemical Parameters and Indexical Approaches in El Manzala Lake, Egypt.
Bibliographic record
Abstract
Physiochemical parameters and the aquatic water quality index (AWQI) were employed to evaluate the surface water purity and identify the various geo-environmental factors influencing the ecological system in El Manzala Lake during two years 2020 and 2021. Water samples were obtained from 11 points, which collected around El Manzala Lake. The obtained analytical results reflected that the surface water in El Manzala Lake was of the semi-saline water type. The physicochemical data such as, T °C, pH, TDS, DO, COD, NO3, NO2, NH4, Cd, Cr, Cu, Fe, Pb, Mn, Ni, Zn revealed mean values of 23.33, 8.3, 8378.05, 6.76, 97.81, 0.11, 0.08, 1.09, 0.00, 0.00, 0.01, 0.05, 0.00, 0.01, 0.01, and 0.01mg/L respectively in the order of Fe > Zn > Mn >Cu >Ni >Pb > Cr> Cd. The concentrations of trace elements in the collected water samples varied considerably, suggesting that the obtained samples were polluted by Cd, Cr, Cu, Fe, Pb, Mn, Ni, and Zn at levels exceeding the acceptable limits recommended by the Canadian Council of Ministers of the Environment (CCME). Based on AWQI results across two years, about 45% of the water samples were classified as unsuitable, 36% of samples were very poor water, and 18% of samples were poor water for use in aquatic environments. As untreated urban and agricultural wastewaters flowed into the lake, the AWQI values increased from the northwest to the southeast directions, indicating a decline in the quality of the water close to the drainages downstream.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".