Review of proposed different irradiation methods to inactivate food‐processing viruses and microorganisms
Bibliographic record
Abstract
Coronaviruses, which have been enveloped nonsegmented positive-sense RNA viruses, were first mentioned in the mid-1960s and can attack people as well as a wide range of animals (including mammals and birds). Three zoonotic coronaviruses have been identified as the cause of large-scale epidemics over the last two decades: Middle East respiratory syndrome (MERS), severe acute respiratory syndrome (SARS), and swine acute diarrhea syndrome (SADS). Epithelial cells in the respiratory and gastrointestinal tract are the principal targeted cells, and viral shedding occurs via these systems in diverse ways such as through fomites, air, or feces. Patients infected with the novel coronavirus (2019-nCoV) reported having visited the Wuhan seafood wholesale market shortly before disease onset. The clinical data on established 2019-nCoV cases reported so far indicate a milder disease course than that described for patients with SARS-CoV or MERS-CoV. This study aimed to review radiation inactivation of these viruses in the food industry in three sections: visible light, ionizing radiation (alpha ray, beta ray, X-ray, gamma ray, neutron, plasma, and ozone), and nonionizing radiation (microwave, ultraviolet, infrared, laser light, and radiofrequency). Due to the obvious possibility of human-to-human and animal-to-human transmission, using at least one of these three methods in food processing and packaging during coronavirus outbreaks will be critical for preventing further outbreaks at the community level.
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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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".