Mathematical modelling by convection-diffusion with reaction of organic pollution in the wadi Mouillah stream north-western Algeria
Bibliographic record
Abstract
This work describes the behaviour of organic pollutants along the wadi Mouillah watercourse and its main tributaries and their impacts on the Hammam Boughrara dam, located in the NW of Algeria, in the Wilaya of Tlemcen. The use of a database relating to physico-chemical, biotic and hydrological variables, covering the period from January 2006 to December 2009, contributed to the understanding of the spatiotemporal evolution of each variable. The application of a mathematical model of the diffusion by convection-dispersion with a reaction on two characteristic parameters of organic pollution, the biochemical oxygen demand (BOD 5) which records values above the norm, with peaks that can reach 614%, and total phosphorus (P tot), which the concentration is always higher with maxima reaching 53 mg∙dm –3 favouring eutrophication; this made it possible with precision to synthesise the propagation of pollutants in the liquid mass. The results obtained on the waters of Wadi Mouillah are therefore of poor quality; there is a need to set up a rigorous water quality monitoring system, with water treatment and decontamination devices to preserve the water resources. This will allow to contribute to better management of water quality in terms of combating the spread of pollution. Therefore, they can be used to support decisions in the context of sustainable development.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".