Editorial: Campylobacter-associated food safety
Bibliographic record
Abstract
Campylobacter is one of the leading bacterial causes of gastroenteritis worldwide. As the commensal in the gastrointestinal tract of warm-blooded animals, especially food-producing animals, Campylobacter can be transmitted to humans through the food supply chain and cause human campylobacteriosis. Although Campylobacter typically causes self-limiting gastroenteritis, it can also lead to prolonged postinfectious complications, such as Guillain-Barré syndrome, reactive arthritis, and/or post infectious-irritable bowel syndrome, posing a great threat to public health. To address the potential risks associated with this disease, numerous studies have been conducted to improve our understanding of this microbe and its interaction with the agri-food system. This mini-review acts as the editorial summary of the published articles in this special issue collected in Frontiers in Microbiology and provides a brief overview of 1) improved detection methods; 2) prevalence and characterization; 3) novel intervention strategies of Campylobacter in the agri-food settings. This special issue is timely due to the increased recognition of Campylobacter organisms as a serious health threat by the World Health Organization, Centers for Disease Control and Prevention, Health Canada, and European Union.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.024 | 0.020 |
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".