Stockage et stabilité à long terme de boues d’épuration municipales décontaminées et stabilisées par voie chimique ou biologique
Bibliographic record
Abstract
The objective of this research was to evaluate the effects of sewage sludge storage on biological and physico-chemical parameters of sludge treated by different stabilization and decontamination processes. Three sludge storage methods were tested: inside at stable temperature (21 ± 2 °C), outside during summer (May–October), and inside at different temperatures to simulate the winter season. An increase of the sludge pH (approximately 8.0–8.5) and an odour removal process were measured following a 5-month storage period, and this, for all conditions of sludge treatments and storage options. The sanitary conditions of the sludge improved, because the fecal coliforms were completely eliminated, and fecal streptococci and salmonella were significantly reduced. The results also revealed that the pH adjustment, near neutrality, of sludge treated by acid processes (Stabiox and Metix-AC) is preferable before a long storage period. This allows a better biodegradation of the organic matter and the production of less odorous sludge. Sludge neutralization also decreases phosphorus and metals concentrations in drainage waters. The preservation of the acid conditions remains however useful during a short storage period, by allowing to avoid a fast resumption of the nauseous odours. Overall, the different temperatures had few effects on sludge characteristics after the 5-month storage period.
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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 | 0.001 |
| 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 | 0.001 | 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".