Contribution to the Study of the Environmental Impact of Microbiological Pollution of the Water in the Lukaya River, Kinshasa DR of Congo
Bibliographic record
Abstract
The data used in this work were collected between the month of January and February of the year 2016 in the Lukaya River, located in the commune of Mont-ngafula, in the city province of Kinshasa. The DRC does not have a specific law or a water code and a clear national policy on integrated water resources management. Several projects exist and are underway with the support of German cooperation. The framework for the application of the laws of the related sectors is hardly applied this favors pollution, the irrational exploitation of fishery resources, inappropriate use of chemicals raising hygienic and environmental concerns. The objective of this work is to assess the environmental impact of the microbiological pollution of the water in the Lukaya River. The water samples were taken from the different sites in 600 ml plastic Canadian bottles and their analysis was performed at the INRB laboratory and the approach adopted in this work is that of membrane filtration which led to the following results: a high bacteriological concentration and numerous pathogens such as Escherichia coli, Proteus vulgaris, Enterobacter, Proteus penneri, Citrabacter and many other bacteria which testify to faecal contamination such as coliforms and faecal streptococci.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".