Determination of bacteria in <i>Corbicula fluminea</i> tissue using 16S rRNA gene sequences
Bibliographic record
Abstract
Corbicula fluminea is a highly consumed traditional snack by the locals in Kelantan, Malaysia. However, the consumption is suspected to be a cause of diarrhoeal disease that originated from bacterial contamination in C. fluminea tissue. Poor handling and unhygienic processing were hypothesised to contribute largely to the bacterial contamination. Hence, this study aims to compare the bacterial community in C. fluminea tissue at each processing stage to determine the root of contamination source in C. fluminea tissue. Polymerase chain reaction was performed to amplify the hypervariable regions V3–V4 of the 16S rRNA gene from bacterial DNA isolated from C. fluminea tissue. The communities at each processing stage were sequenced on the Illumina Miseq platform and then analysed using the Divisive Amplicon Denoising Algorithm 2 pipeline. The sequencing regions were examined using the Silva v132 reference database. A total of 4 195 108 raw reads were obtained from six bacterial DNA samples of C. fluminea, and the assembled reads were assigned to 888 amplicon sequence variants. Proteobacteria (87.8%), Firmicutes (8%) and Bacteroidetes (3.1%) were the most dominant groups at the phylum level, while Aeromonas (47%), Klebsiella (15.7%) and Enterobacter (10.1%) were predominant at the genus level. The presence of these pathogenic bacteria in the C. fluminea, especially in the smoking and selling stages indicate unhygienic handling by human during preparation and selling stages and this could pose a health risk to consumers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".