Composting organic residues: Trace metals and microbial pathogens
Bibliographic record
Abstract
Industrialized societies are responsible for increasing environmental pollution by trace metals, and pathogenic micro-organisms associated with products and wastes of industrial processes and agricultural production, as well as household activities. The objective of this paper is to discuss the effect of composting on the behavior of trace metals and pathogenic micro-organisms, and to present the latest finding on sanitizing and stabilizing organic fractions and trace metals in composted biosolids. Evidence provided by long-term scientific studies in North America and Europe on toxic effects of trace metals on soil micro-organisms and on pathogenic micro-organisms suggests that revisions of the current guidelines on sludge applications on agricultural soils are needed to protect long-term soil fertility. In addition, the antagonism between harmful characteristics of biosolids and their usefulness for agriculture and land reclaiming purposes could be mitigated by using efficient composting treatments. The production and use of harmless organic wastes should be one of our highest priorities in order to protect our health and environment, and to hand over a sustainable agriculture to future generations. Key words: Composting, biosolids, pathogenic micro-organisms, trace metals, sanitization
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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 | 0.001 | 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".