Driving forces shaping the microbial ecology in meat packing plants
Bibliographic record
Abstract
Meat production is a complex system, continually receiving animals, water, air, and workers, all of which serve as carriers of bacteria. Selective pressures involved in different meat processing stages such as antimicrobial interventions and low temperatures, may promote the accumulation of certain residential microbiota in meat cutting facilities. Bacteria including human pathogens from all these sources can contaminate meat surfaces. While significant advancements have been made in enhancing hygienic standards and pathogen control measures in meat plants, resulting in a notable reduction in STEC recalls and clinical cases, STEC still stands as a predominant contributor to foodborne illnesses associated with beef and occasionally with pork. The second-and third-generation sequencing technology has become popular in microbiota related studies and provided a better image of the microbial community in the meat processing environments. In this article, we reviewed the potential factors influencing the microbial ecology in commercial meat processing facilities and conducted a meta-analysis on the microbiota data published in the last 10 years. In addition, the mechanisms by which bacteria persist in meat production environments have been discussed with a focus on the significant human pathogen E. coli O157:H7 and generic E. coli , an indicator often used for the hygienic condition in food production.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".