Accessibilité et qualité microbiologique des eaux de consommation dans la zone de santé Lubumbashi à Lubumbashi RDC
Bibliographic record
Abstract
Il est question de l’évaluation de l’accessibilité ainsi que de la qualité microbiologique des eaux de consommation dans la zone de santé Lubumbashi. L’étude de l’accessibilité et de la qualité des eaux de consommation dans la zone de santé de Lubumbashi a consisté en une enquête auprès de 646 ménages et des analyses microbiologiques de 10 échantillons d’eau au cours de deux saisons durant la période allant de Janvier à Avril 2017 pour la saison pluvieuse et de Juin à Septembre 2017 pour la saison sèche. De l’étude descriptive transversale effectuée, l’eau de robinet est la source principale d’eau (74%). L’eau est rare entre Mai et Août (37,9% d’accès). Pendant la saison sèche 61,6% de ménages utilisent moins de 20 litres par jour et par personne pour tous usages et 62,5% parcourent plus de 200 mètres pour arriver à la source d’approvisionnement en eau. Les analyses microbiologiques ont indiqué que 60% des échantillons étaient non potables pendant la saison pluvieuse contre 30% pendant la saison sèche. Abstract This concerns the evaluation of accessibility as well as the microbiological quality of drinking water in the Lubumbashi health zone. The study of the accessibility and quality of drinking water in the Lubumbashi health zone consisted of a survey of 646 households and microbiological analyzes of 10 water samples during two seasons during the period from January to April 2017 for the rainy season and from June to September 2017 for the dry season. From the cross-sectional descriptive study carried out, tap water is the main source of water (74%). Water is rare between May and August (37.9% access). During the dry season 61.6% of households use less than 20 liters per day per person for all purposes and 62.5% travel more than 200 meters to reach the water supply source. Microbiological analyzes indicated that 60% of the samples were non-drinkable during the rainy season compared to 30% during the dry season.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".