Microbiological quality of blue mussels (Mytilus edulis) in Nunavik, Quebec: a pilot study
Bibliographic record
Abstract
This pilot study was aimed at documenting the presence of fecal indicators and enteric pathogens in blue mussels (Mytilus edulis) from 6 communities in Nunavik, Quebec. One to four 2 kg samples of mussels were collected at low tide in each community. Samples were investigated by enumeration methods for the fecal indicators enterococci, Escherichia coli, F-specific coliphages, Clostridium perfringens, and by molecular identification for the pathogens norovirus, Salmonella spp., Campylobacter jejuni, Campylobacter coli, and Campylobacter lari, verocytotoxin-producing E. coli (particularly serovar O157:H7), Shigella spp., and Yersinia enterocolitica. In 5 communities, the presence of Giardia duodenalis and Cryptosporidium spp. was also tested by microscopy and molecular methods and that of Toxoplasma gondii was tested by molecular methods. Apart from small quantities of Clostridium perfringens in 2 samples, no bacterial or viral pathogens were detected in the mussels. Toxoplasma gondii was also not detected. However, G. duodenalis and Cryptosporidium spp. were present in 18% and 73% of the samples investigated for these pathogens, respectively. When considering the indicators and the viral and bacterial pathogens investigated, the mussels examined were of good microbiological quality, but considering the presence of potentially zoonotic protozoa, it should be recommended that consumers cook the molluscs well before eating them.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".