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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".