Bibliographic record
Abstract
Vermicomposting, the non-thermophilic decomposition of organic wastes by earthworms, is a popular waste management option in the Americas, Europe and the Indian sub-continent. Although the technology is inexpensive and produces an organic fertilizer as well as earthworm biomass, there are few examples of vermicomposting in sub-Saharan Africa. The objective of this thesis was to investigate the potential for vermicomposting in Accra, the capital city of Ghana, by conducting 1) an earthworm survey, 2) vermicomposting trials and 3) assessing farmers' perceptions of vermicompost as an organic fertilizer and other related issues. The earthworm Eudrilus eugeniae (Kinberg), was found in the soil-litter layer at seven locations across Accra. In a 20 d period, the E. eugeniae decomposed 99% of pineapple fibers and 87% of pineapple peels supplied, indicating that this earthworm is capable of vermicomposting. The nutrient value of the vermicompost was low, relative to other organic wastes in West Africa, probably due to the low nutrient content of pineapple wastes. Farmers were aware of the benefits to soil fertility from earthworm activity and associated the presence of earthworm castings with healthy soils. However, those involved in irrigated vegetable farming had insufficient space and time for on-farm vermicomposting and would prefer to purchase this fertilizer. Conversely, subsistence farmers lacked a reliable access to water necessary for on-farm vermicomposting. In summary, farmers were interested in the technology and were willing to adopt it, provided the vermicompost improved crop performance and was affordable and available.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".