Application of host-specific genetic markers for microbial source tracking of faecal water contamination in an agricultural catchment
Why this work is in the frame
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Bibliographic record
Abstract
Elevated nutrient concentrations in streams in the Norwegian agricultural landscape may occur due to faecal contamination. Escherichia coli (E. coli) has been used conventionally as an indicator of this contamination; however, it does not indicate the source of faecal origin. This work describes a study undertaken for the first time in Norway on an application of specific host-associated markers for faecal source tracking of water contamination. Real-time quantitative polymerase chain reaction (qPCR) on Bacteroidales host-specific markers was employed for microbial source tracking (MST) to determine the origin(s) of faecal water contamination. Four genetic markers were used: the universal AllBac (Bacteroidales) and the individual specific markers BacH (humans), BacR (ruminants) and Hor-Bac (horses). In addition, a pathogenicity test was carried out to detect the top seven Shiga toxin-producing E. coli (STEC) serogroups. The ratio between each individual marker and the universal one was used to: (1) normalise the markers to the level of AllBac in faeces, (2) determine the relative abundance of each specific marker, (3) develop a contribution profile for faecal water contamination and (4) elucidate the sources of contamination by highlighting the dominant origin(s). The results of the qPCR MST analyses indicated the actual contributions of humans and animals to faecal water contamination. The pathogenicity test revealed that water samples were STEC positive at a low level, which was in proportion to the concentration of the ruminant marker. The outcomes were verified statistically by coupling the findings of major contamination sources with observations in the field regarding local land use (residential or agricultural). Furthermore, clear correlations between the human marker and E. coli counts as well as the ruminant marker and STEC quantity in faecally contaminated water were observed. The results of this study have the potential to help identify sources of pollution for targeted mitigation of nutrient losses.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it