Identification of the origin of faecal contamination in estuarine oysters using <i>Bacteroidales</i> and F-specific RNA bacteriophage markers
Bibliographic record
Abstract
AIMS: The aim of this study was to identify the origin of faecal pollution impacting the Elorn estuary (Brittany, France) by applying microbial source tracking (MST) markers in both oysters and estuarine waters. METHODS AND RESULTS: The MST markers used were as follows: (i) human-, ruminant- and pig-associated Bacteroidales markers by real-time PCR and (ii) human genogroup II and animal genogroup I of F-specific RNA bacteriophages (FRNAPH) by culture/genotyping and by direct real-time reverse-transcriptase PCR. The higher occurrence of the human genogroup II of F-specific RNA bacteriophages using a culture/genotyping method, and human-associated Bacteroidales marker by real-time PCR, allowed the identification of human faecal contamination as the predominant source of contamination in oysters (total of 18 oyster batches tested) and waters (total of 24 water samples tested). The importance of using the intravalvular liquids instead of digestive tissues, when applying host-associated Bacteroidales markers in oysters, was also revealed. CONCLUSIONS: This study has shown that the application of a MST toolbox of diverse bacterial and viral methods can provide multiple lines of evidence to identify the predominant source of faecal contamination in shellfish from an estuarine environment. SIGNIFICANCE AND IMPACT OF THE STUDY: Application of this MST toolbox is a useful approach to understand the origin of faecal contamination in shellfish harvesting areas in an estuarine setting.
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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 | 0.000 | 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 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".