Biosolids from Two-stage Bioleaching could produce Compost for Unrestricted Use
Bibliographic record
Abstract
A biological process, called BIOSOL, developed in our laboratory, efficiently reduces metals and reduces pathogenic indicator bacteria in sewage sludges in a single-stage system to levels meeting Ontario Ministry of the Environment (OME) requirements for land application of biosolids. Since the requirements for unrestricted use of compost are 10 to 50 times (depending on the metal involved) more restrictive than those for land application, this study was carried out to determine whether the efficiency of the BIOSOL system could be increased to meet the more stringent limitations required for compost. A two-stage modified BIOSOL system was successfully operated for a period of 4% months, at 30 degrees C, treating anaerobically digested sludge from the Guelph Wastewater Treatment Plant. Elemental sulphur (4g l(-1)) was the energy source for the autotrophic bacteria (thiobacilli). The pH, metals (Cd, Cr, Cu, Pb and Zn), and sulphate were used to evaluate system effectiveness. At an HRT of 8 days in each solubilization tank, and a S*-concentration of 4g l(-1), sulphur oxidation efficiency was about 70%, while the metal removals were: Cd 90%, Cr 93%, Cu 96%, Pb 67% and Zn 98%. The finished product (biosolids) met the OME requirements for metal concentrations for producing compost or fertilizer for unrestricted use. The reduction of pathogenic indicator organisms was demonstrated in earlier studies.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".