Natural Soil Reservoirs for Human Pathogenic and Fecal Indicator Bacteria
Bibliographic record
Abstract
Soils receive inputs of human pathogenic and indicator bacteria through land application of animal manures or sewage sludge, and inputs by wildlife. Soil is an extremely heterogeneous substrate and contains meso- and macrofauna that may be reservoirs for bacteria of human health concern. The ability to detect and quantify bacteria of human health concern is important in risk assessments and in evaluating the efficacy of agricultural soil management practices that are protective of crop quality and protective of adjacent water resources. The present chapter describes the distribution of selected Gram-positive and Gram-negative bacteria in soils. Methods for detecting and quantifying soilborne bacteria including extraction, enrichment using immunomagnetic capture, culturing, molecular detection and deep sequencing of metagenomic DNA to detect pathogens are overviewed. Methods for strain phenotypic and genotypic characterization are presented, as well as how comparison with clinical isolates can inform the potential for human health risk
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".