Detection of foodborne viruses in berries – State of science and future considerations
Bibliographic record
Abstract
Enteric viruses are the leading cause of foodborne disease, with human norovirus (HuNoVs) the most prevalent, and hepatitis A virus (HAV) the more severe. Fresh and frozen berry fruits are a recognized vehicle for transmission, gaining increased international attention. The detection of these viruses is complicated because: (i) they cannot be cultivated routinely in vitro ; (ii) their concentrations in foods are frequently low; (iii) and sample matrices are complex. ISO- standardized methods, released in the last decade, are widely used, but there remain complexities in their applications, interpretations, and risk-based decision making based on results. This paper describes deliberations of an International Expert Panel asked to address the following: (i) methods most often used to detect viruses in fresh and frozen berries; (ii) role of sampling in test reliability; (iii) means by which testing results are interpreted; (iv) typical uses of testing by various stakeholder sectors; (v) role/use of confirmatory testing; (vi) how testing results are used by various stakeholder sectors; and (vii) the overall value of testing. Critical unanswered questions are discussed, such as the relationship between RT-qPCR positive results and infection risk (virus infectivity) and the role of testing in risk management. Perhaps the most comprehensive work of its kind, this paper highlights the unique challenges posed by emerging molecular-based detection methods applied to non-cultivable foodborne pathogens and sets a stage for the questions that beg answers as these methods become more widely and routinely used.
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 | 0.051 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".