Microbial community transfers across a pilot ripening cellar are increased by cheese wiping
Bibliographic record
Abstract
Various biological agents (bacteria, molds, yeasts...) contribute by their metabolic activity to cheese’s ripening. Cheese care procedures during ripening, like wiping, may disperse microorganisms from cheese rinds. Chronic inhalation of potential allergenic particles in ripening cellars may cause, for operators, development of respiratory diseases as asthma. However, microorganisms’ emissions and transfers across ripening cellars during cares remained poorly documented. To evaluate microorganisms transfer consecutive to cheese wiping, we focused on microbial community from long-ripened cheeses (CH_LR) and its dispersion in air and on short-ripened cheeses (CH_SR). Twenty-four short-ripened cheeses, all wiped, were distributed into 3 experimental cellars (INRAE, Aurillac), two of which also received 6 long-ripened cheeses either wiped (cellar 2) or unwiped (cellar 3). Samples were taken over a period of 4 weeks in four environments: cheese rinds, cheese cloths, air and cellar walls. Levels of culturable microorganisms were assessed (n=92). Microbial community compositions were analyzed by metabarcoding (16S rRNA and ITS genes, respectively) (n=100 samples). Results showed an increase in airborne mold levels up to 7 log CFU.m -3 of air during cheese wiping, compared to 2-3 log CFU.m -3 without wiping activity. Microbial profiles analyses revealed dominant species on CH_LR such as Mucor , Penicillium and Glutamicibacter sp. In CH_LR, Glutamicibacter sp. (60%), Mucor sp and Penicillium sp (50% altogether), were transferred to air (respectively 60% for Glutamicibacter sp and 90% for both fungal species), cheese cloths and CH_SR. Wiping of CH_LR also contributed to the dispersion in air of less abundant genera of interest for cheese ripening like Chrysosporium (<10%).
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".