Application of molecular technologies to monitor the microbial content of biosolids and composted biosolids
Bibliographic record
Abstract
Disposal of human biosolids is a source of concern for public health and the environment. Composting appears to be an interesting alternative to traditional disposal methods as it can decrease the load of human pathogenic microorganisms often present in biosolids and yield an end-product rich in nutrients for use as a soil supplement. Assessing the exact microbial content of biosolids, both for biosafety and operational reasons, has traditionally relied on the use of standard microbiological methods. Recent developments in molecular-based technologies now offer more rapid and specific monitoring of microorganisms in biosolids than culture-based methods. In this study, denaturing gradient gel electrophoresis (DGGE) was adapted to monitor the succession of bacteria in composted biosolids through different steps of compost production. Secondly, a TaqMan quantitative real time PCR (qPCR) approach was developed to detect and quantify the presence of Salmonella species, a model human pathogenic bacterium, susceptible to be found in biosolids. DGGE results indicated that the bacterial content of composted biosolids of different ages belongs to various taxa and significantly changes with age. qPCR results indicated that the quantity of Salmonella species found in composted biosolids ranging from 1 to 24 months significantly decreases with composting time.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".