Agronomic value of composts made from household solid wastes and local organic additives in Goma City, Democratic Republic of Congo
Bibliographic record
Abstract
Rapid population growth often leads to an increase in municipal waste, which can cause environmental pollution if not properly managed. Recycling waste is therefore essential to reduce pollution and to create useful products. This study aimed to evaluate the quality of composts produced from household solid waste (HSW) and local organic additives for use as fertilizers. A field experiment was conducted using a completely randomized block design with three replications, comprising three treatments: T0 (100% HSW), T1 (70% HSW + 30% cattle manure [CM]), and T2 (70% HSW + 30% poultry manure [PM]). The results showed that the final composts had neutral to slightly alkaline pH values, except for the compost made from HSW alone, which was closer to neutral. The composts contained organic matter (OM), nitrogen (N), and potassium (K) at levels acceptable according to AFNOR standards, with nutrient content increasing with the addition of organic amendments; T2 exhibited the highest values. Regardless of the treatment, the concentrations of Copper (Cu), Zinc (Zn), and Iron (Fe) exceeded the AFNOR standard limits. Overall, composts derived from HSW in Goma are nutrient-rich and can serve as effective organic fertilizers capable of restoring soil fertility. Key words: Household solid waste, cattle manure, poultry manure, compost, waste management, Goma, Democratic Republic of Congo.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".