Prevalence and abundance of lactic acid bacteria in raw milk associated with forage types in dairy cow feeding
Bibliographic record
Abstract
Lactic acid bacteria (LAB) found in milk can be responsible for organoleptic defects in cheese. To identify sources of LAB that could potentially develop during cheese making, we evaluated their prevalence and abundance in milk according to the type of forage used in dairy cow feeding. Forages and bulk tank milk were sampled 3 times on 24 farms using either hay alone (control), or grass or legume silage supplemented with corn silage or not. Both types of silage were either non-inoculated or inoculated with commercial preparations containing at least a Lactobacillus buchneri strain along with Lactobacillus casei, Lactobacillus plantarum, Enterococcus faecium, or Pediococcus pentosaceus. Our results indicate that LAB viable counts in milk samples (2.56 log cfu/mL) did not differ according to the type of forage used. A total of 1,239 LAB were isolated and identified by partial 16S rRNA gene sequencing. Although inoculation increased lactobacilli abundance in grass silage by 35%, we did not observe an effect on the LAB profile of milk. Indeed, we found no significant difference in milk LAB prevalence and abundance according to the type of forage (P > 0.05). Moreover, isolates belonging to the L. buchneri group were rarely found in bulk tank milk (3 out of 481 isolates). Random amplified polymorphic DNA typing of 406 LAB isolates revealed the plausible transfer of some strains from silage to milk (~6%). Thus, forage is only a minor contributor to LAB contamination of milk.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".