Class A Pathogen Reduction in the SSDML Process
Bibliographic record
Abstract
The agricultural use of sewage sludge is limited by the presence of toxic metals and pathogens. The simultaneous sludge digestion and metal leaching (SSDML) process has been developed to resolve this problem. In the present study, the performance of the SSDML process for the elimination of total coliforms, fecal coliforms, fecal streptococci, and coliphages from sewage sludge was verified by shake flask, laboratory bioreactor, and pilot plant bioreactors. The effect of pH, sludge solids concentration, temperature, and sludge types on the bacterial indicator's removal was evaluated. The results showed that the SSDML process was more efficient than the conventional mesophilic aerobic sludge digestion process for the elimination of bacterial (2.5 to >7.0 log units reduction) and viral (>8.0 log units reduction) indicator microorganisms.
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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 | 0.000 | 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 | 0.001 |
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".