Identification of human fecal pollution sources in a coastal area: a case study at Oostende (Belgium)
Bibliographic record
Abstract
From April to June 2001, a monitoring study at Oostende (Belgium) was conducted to obtain an insight into fecal pollution impairing water quality at this coastal area. Eight sampling sites were selected based on the historically low water quality at these sites compared to the remainder of the area. Indicator organisms such as fecal coliforms, Escherichia coli and fecal streptococci were monitored by plating. A real-time PCR assay for quantification of the human-specific HF183 Bacteroides 16S rRNA genetic marker was used to detect human fecal pollution at the sampling sites. Human fecal pollution was detected at all sampling sites. However, the frequency of detection ranged from 30-100% and the amount of human-specific Bacteroides markers recorded varied between the sampling sites. Concentrations of 10(7) human-specific Bacteroides markers per 1 to levels below the detection limit of the real-time PCR assay were recorded. Our results indicate that human fecal pollution is a re-occurring problem in certain areas. Of all the environmental parameters monitored during the study, only rainfall was strongly related to the detection of the indicator organisms and the human-specific Bacteroides marker.
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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 | 0.001 |
| 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 | 0.001 |
| 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".