Surface microbial consortia from Livarot, a French smear-ripened cheese
Bibliographic record
Abstract
The surface microflora (902 isolates) of Livarot cheeses from three dairies was investigated during ripening. Yeasts were mainly identified by Fourier transform infrared spectroscopy. Geotrichum candidum was the dominating yeast among 10 species. Bacteria were identified using Biotype 100 strips, dereplicated by repetitive extragenic palindromic PCR (rep-PCR); 156 representative strains were identified by either BOX-PCR or (GTG)(5)-PCR, and when appropriate by 16S rDNA sequencing and SDS-PAGE analysis. Gram-positive bacteria accounted for 65% of the isolates and were mainly assigned to the genera Arthrobacter , Brevibacterium , Corynebacterium , and Staphylococcus . New taxa related to the genera Agrococcus and Leucobacter were found. Yeast and Gram-positive bacteria strains deliberately added as smearing agents were sometimes undetected during ripening. Thirty-two percent of the isolates were Gram-negative bacteria, which showed a high level of diversity and mainly included members of the genera Alcaligenes , Hafnia , Proteus , Pseudomonas , and Psychrobacter . Whatever the milk used (pasteurized or unpasteurized), similar levels of biodiversity were observed in the three dairies, all of which had efficient cleaning procedures and good manufacturing practices. It appears that some of the Gram-negative bacteria identified should now be regarded as potentially useful in some cheese technologies. The assessment of their positive versus negative role should be objectively examined.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".