Coagulation of turbid waters using Moringa oleifera seeds from two distinct sources
Bibliographic record
Abstract
Samples of turbid water prepared under laboratory controlled conditions were tested using natural coagulant-flocculant Moringa oleifera seeds from Burundi, Central Africa, and from Mahajanga, Madagascar. Coagulation-flocculation and sedimentation experiments were conducted using jar test equipment. For these tests, 5% Moringa oleifera solutions (w/w in water) were prepared using shelled and non-shelled seeds from the aforementioned countries. The results show that, in both cases, the shelled seeds provide much higher turbidity removal than the non-shelled ones. In addition, the volume of sludge produced was approximately 30% of that of conventional coagulants such as alum. Finally, it was concluded that seeds from Burundi were of superior quality than those of Madagascar. In fact, higher dosages of these seeds, of up to four times, were required in order to attain the same level of turbidity as the Burundi seeds.
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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 | 0.000 |
| 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 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".