Waste degradation and leachate quality on composting platform: A case study in Lome, Togo
Bibliographic record
Abstract
Chemical or organic compounds released during the waste composting could be harmful to the environment. The purpose of this work is to investigate the stability indices of organic matter after the lixiviation runs on composting platforms. The results showed that the organic matter became resistant to biodegradation after 2 months of wastes composting, that the nitrates content decreased, while the phosphorus one increased. The organic load predominantly hydrophilic substances (HPI*) decreased significantly (p <0.05) during composting polymerizing into humic acids (AH characteristics of the leachate, such as a pH <8, a (BOD5/COD) ratio >0.1 and a (AH*+HPO*) content <50% pointed to the class of an intermediate leachate. However, the values of COD*) and hydrophobic substances (HPO*). The increase in (AH*+HPO*) fractions is correlated with Specific UV Absorbance (SUVA) value which rose from 4 to 25 L.gC-1.cm-1. Some <4000 mgO2.L-1, of SUVA >20 L.gC-1.cm-1 and an HPI* concentration <30% suggested a stabilized leachate. Thus, during the 4 months, the resultant compost although mature was not stable. The leachate characterization has permitted to understand both its quality and the state of the waste where it originates from, making it possible to prevent any chemicals leakage into the environment. Key words: Waste, composting, organic matter, hydrophobic fractionation.
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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 | 0.001 | 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".