Physicochemical Characterization and Assessment of Magnitude of Pollution to Contribute to Water Sustainability
Bibliographic record
Abstract
As in many countries worldwide, deterioration of quality of water in Morocco’s natural reserves continues, such that an inventory of qualities of these reserves has become necessary. Based on statistical analyses of the data collected by measuring nineteen physicochemical variables, and applying an index, the comprehensive pollution index, to those data, the quality of the waters of Lake Dayat Roumi, Morocco, were assessed. Waters of the lake are currently characterized by a salinity of 921.7 mg Cl−/L and 124.2 mg sulfates/L. The concentration of chloride exceeds the value indicated in the standard norms. This can affect the composition of the sediment, overlying water, and specific composition of biota in the lake. Status and trends of values of other parameters also indicate deterioration of water quality in this lake. Multivariate statistical analysis made it possible to differentiate four clusters of samples according to their physicochemical characteristics. These groups are due to inputs from three sources of materials: leaching of rocks from the watershed and plantations, discharge of domestic wastewater and runoff water. Samples taken from deeper parts of the lake indicate a depression of concentrations of dissolved oxygen during the hot season. The analysis results demonstrate the heterogeneous degradation of the water quality in the peripheral areas and deep parts of the lake, favored by the leaching of soils by rainwater, which, when sufficiently severe, can cause repeated mortalities of fishes. This situation requires regular water quality monitoring to develop a management plan for restoration of water quality in the lake to preserve valued ecological services.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".