Review of proposed different irradiation methods to inactivate food‐processing viruses and microorganisms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it