Biofilm Formation in Dairy: A Food Safety Concern—Microbial community tracking from dairy farm to factory; Insights on biofilm management for enhanced food safety and quality
Bibliographic record
Abstract
This review aimed to assess the scope of the literature on tracking the microbial community of biofilms, focusing on the dairy farm and processing environments. The majority of studies focused on either production, storage, transport, or processing of milk, and 5 studies combined the investigation of both production and processing facilities. Factors influencing short-term changes in dairy microbiota, such as the occurrence of mastitis and season, were distinguished from factors revealed through long-term studies, such as feed and weather, rather than the milking equipment. Knowledge gaps were identified in relation to the study design, methods, data analysis, and interpretation. The application of DNA sequencing technologies is particularly challenging with respect to samples with low microbial load (milk, swabs). There are few studies on the microbial composition of in situ biofilms, which might require new technologies for detection before sampling. Fundamental studies on the structure of biofilms are needed to identify the on-farm practices affecting the cycle of biofilm development in milking systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".