Microflore hétérotrophe impliquée dans le procédé simultané de biolixiviation des métaux et de digestion des boues d'épuration
Bibliographic record
Abstract
Until now, some microbiological studies have made it possible to highlight the role and identification of certain chimiolithotroph microorganisms directly involved in the simultaneous sewage sludge digestion and metal leaching (SSDML process). Moreover, some other studies have established the performance of the SSDML process for the destruction of pathogens. However, until now no study has been carried out to define the equilibrium of the heterotrophic microbial populations during the sludge acidification occurring during the operation of the SSDML process. Hence, the SSDML process was studied in batch and continuous mode employing different types of sludges (Communauté Urbaine de Québec (CUQ)-East primary sludge, Beauceville secondary sludge, and mixture of primary and secondary Valcartier sludge). The equilibrium of heterotrophic microbial populations was investigated along with sludge acidification and solids degradation. The results obtained during the SSDML process on different sludges showed a decrease followed by the destruction of a majority of heterotrophic bacterial species found in fresh sludges. The isolation and characterization of different microbial species showed that only two types of microorganisms persisted: the yeast Blastoschizomyces capitatus and an unidentified fungus.Key words: sewage sludge, simultanous bioleaching and digestion, heterotrophic bacteria, Blastoschizomyces capitatus, fungi.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".