Analysis of Microbiota Persistence in Quebec’s Terroir Cheese Using a Metabarcoding Approach
Bibliographic record
Abstract
Environmental short amplicon sequencing, or metabarcoding, is commonly used to characterize the bacterial and fungal microbiota of cheese. Comparisons between different metabarcoding studies are complicated by the use of different gene markers. Here, we systematically compare different metabarcoding molecular targets using V3-V4 and V6-V8 regions of the bacterial 16S rDNA and fungal ITS1 and ITS2 regions. Taxonomic profiles varied depending on the molecular markers used. Based on data quality and detection capacity of the markers toward microorganisms usually associated with the dairy environment, the ribosomal regions V3-V4 and ITS2 were selected and further used to evaluate variability in the microbial ecosystem of terroir cheeses from the province of Quebec in Canada. Both fungal and bacterial ecosystem profiles were described for 32 different ready-to-eat bloomy-, washed- and natural-rind specialty cheese varieties. Among them, 15 were studied over two different production years. Using the Bray-Curtis dissimilarity index as an indicator of microbial shifts, we found that most variations could be explained by either a voluntary change in starter or ripening culture composition, or by changes in the cheesemaking technology. Overall, our results suggest the persistence of the microbiota between the two years studied-these data aid understanding of cheese microbiota composition and persistence during cheese ripening.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".