Dynamic Existence of Waterborne Pathogens within River Sediment Compartments. Implications for Water Quality Regulatory Affairs
Bibliographic record
Abstract
The transport and fate of indicator E. coli and Salmonella are shown to be highly influenced by their relationship with flocculated suspended and bed sediment particles. Flocs were found to dominate the suspended sediment load and have the effect of increasing the downward flux of the sediment to the river bed. Bacteria counts were consistently higher within sediment compartments (suspended and bed) than for the water alone, with the bed sediment found to represent a possible reservoir of pathogens for subsequent remobilization and transport to potentially high risk areas. The mechanism of microbial attachment and entrapment within the sediment was strongly linked to the EPS fibrils secreted by the biological consortium of the aquatic system. It is suggested that the sediment/pathogen relationship should be of concern to public health officials because of its potential effects on pathogen source fate and effect with implications on public health risk assessment. Current standard sampling strategies, however, are based on an assumption that bacteria are entirely planktonic and do not account for the potentially significant concentration of bacteria from the sediment compartments. The lack of understanding around pathogen/sediment associations may lead to an inaccurate estimate of public health risk, and, as such, possible modification of sampling strategies to reflect this association may be warranted.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".