Reduction of Indicator Organisms During Biological Solubilization of Metals: A Pilot Study
Bibliographic record
Abstract
Laboratory and pilot scale studies on the biological solubilization of metals from undigested raw sludge were carried out using elemental sulphur as the energy source. Metals (Cu, Zn, Ni and Cd), pH, sulfate and indicator bacteria [total coliforms (TC) faecal coliforms (FC) and faecal streptococci (FS)] were used to evaluate the system effectiveness. This paper does not report the detailed design and operational data of the pilot plant (available elsewhere [1]) but focuses on the biological tests carried out at the Main Wastewater Treatment Plant in Toronto from July to December, 1997. Since the indicator bacteria are injured from exposure to low pH and potentially toxic metal concentrations, improved alternative methods for their enumeration and their ability to repair acid-metal induced injury in a resuscitation medium (CASO tryptone soya broth) were also investigated. Resuscitation was not effective in repairing cells injured during bacterial leaching. The MPN technique (using lauryl tryptose broth) for enumerating indicator bacteria (total coliforms) gave higher counts and was therefore superior to the spread plate technique (using m-Endo agar) in recovering bacteria from acidic leached sludge (biosolids). However, the coliform bacterial counts from raw sludge were similar by the two methods. This study indicated that the biological solubilization process could significantly reduce the pathogenic indicators. Concentrations of TC, FC and FS in the leached biosolids from the solubilization tank were lower than concentrations in the raw sludge by 4 to 6 orders of magnitude and the finished product (biosolids) met US. EPA requirements for pathogen and metal concentrations for Class A biosolids to be used on agricultural land.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".