Development of Household Defluoridation Unit Based on Crushed Burnt Clay Pot as Sorbent Medium: A Case of Keren Community, Eritrea
Bibliographic record
Abstract
Fluoride in water in some parts of Eritrea is above the WHO guideline of 1.5 mg/L. One of the communities in Eritrea exposed to drinking water of high fluoride is Keren community and as a result, they suffer dental and skeletal fluorosis. A survey at 16 water sources in 13 villages was made and 87% of the samples exceeded the guideline, having fluoride levels 1.40-3.98 mg/L. Fluoride removal from synthetic water using crushed burnt clay pot as a sorbent medium was studied in a packed column. A preliminary experiment was carried out on a laboratory scale in mini column, with three different packed beds, 15, 20 and 25 cm depth. A flow rate of 2.5, 5, 10, and 15 ml/min having 5 mg/L fluoride was passed through each bed. The results indicated that the breakthrough volume and time increased with increasing bed depth of the column. On the other hand, an increase in flow rate reduced the treated volume at breakthrough and therefore decreased the service time. Ideal breakthrough occurred at 25 cm bed depth at a flow rate of 2.5 ml/min with breakthrough volume 7.3 L, resulted in reduction of fluoride from 5 to 1.48 mg/L. The result of the mini column was scaled up and tested in a pilot scale unit. The pilot scale managed to treat 324 L of water satisfying the WHO standards of fluoride concentration. The performance of the pilot column agreed with that obtained from the mini column and therefore, crushed burnt clay pot is suitable low cost adsorbent to remove fluoride from water.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".