Estimation of rainwater harvesting potential for emergency water demand in the era of COVID-19. The case of Dilla town, Southern, Ethiopia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Safe and adequate quantity of water is crucial for the implementation of infection prevention and control measures during the prevention of COVID-19. Rainwater harvesting could be an optional water source to fulfill or support the emergency water demand in areas where there is abundant rainfall. The study aimed to assess the rainwater harvesting potential and storage requirements for households and selected institutions and to determine its adequacy to satisfy the emergency water demand for the prevention of COVID-19 in Dilla town, Southern Ethiopia. Rainwater harvesting potential for households and selected institutions were quantified using 17 years’ worth of rainfall data from the Ethiopian Meteorology Agency. To address the rainfall variability, we computed the confidence limits of monthly harvest-able rainwater potential using confidence intervals about the mean as well as confidence intervals using Coefficient of Variation (COV) of monthly rainfall. The storage requirements were also estimated by considering the driest and west seasons and months. The average annual rainfall in Dilla town was 1464 mm. Households with a roof area of 40 and 100 m2 have the potential to harvest 7.2–39.66 m3 and 19.11–105.35 m3 of rainwater respectively. Similarly, the rainwater harvesting potential for the selected institutions was in the range of 34524.5–190374.5, 4070.8–14964.8 , 1140.4–6288.6, 4561.7–25154.3, 5605.8–14152.8 , and 402.4–2219.1 m3 of rainwater for colleges, vocational schools, secondary schools, primary schools, Dilla University Referral Hospital and health centers respectively. These institutional rainwater harvesting potentials can address, 24–132.2, 222.4 –817.8, 59.4–327.3, 34.6–190.9, 94.5–238.5, and 28.2–155.7 % of the colleges, vocational schools, secondary schools, primary schools, Dilla University referral hospital, and, health centers emergency water demand respectively. Rainwater can be an alternative water source for the town in the prevention and control of COVID-19. Further applied researches must be conducted that can address the rainwater quality and treatment for ease of use.
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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 | 0.000 | 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 | 0.000 |
| 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 it