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Record W3041196140 · doi:10.1136/vr.m2702

Aerosols in meat plants as possible cause of Covid‐19 spread

2020· article· en· W3041196140 on OpenAlexaboutno aff
Alex Donaldson

Bibliographic record

VenueVeterinary Record · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)CitationSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Library science2019-20 coronavirus outbreakAnimal healthVeterinary medicineComputer scienceMedicineVirologyPathology

Abstract

fetched live from OpenAlex

Between March and June 2020, a series of outbreaks of coronavirus (SARS-CoV-2) occurred in meat factories extending from Europe to North America, including those in the USA, Canada, Germany, France, Spain and the UK. Many hundreds of workers were affected in an outbreak at Sioux Falls, South Dakota, USA, and at Tonnies, near Gütersloh, North Rhine-Westphalia, Germany, as well as near High River, Alberta, Canada. In a meat factory in Anglesey, Wales, more than 150 workers were affected. Various reasons have been proposed for the outbreaks, including the crowded working conditions, the cold working environment, workers not wearing masks properly or not at all, and the need for workers to talk loudly over the background noise of machinery. Most experts and commentators seem to be baffled and have spoken about the need for further investigations. While those factors may have been involved there are certain practices within such facilities which, I believe, should also be considered. Common to meat factories is the frequent washing down and brushing of floors and surfaces to maintain hygiene. Those procedures are very effective methods for generating large quantities of droplets and aerosols, especially when high-pressure hosing is employed.1 If the floor or surface is contaminated with coronavirus, perhaps unknowingly, by droplets from one or more infected workers, it is likely that aerosols of infectious particles will be dispersed into the atmosphere and would then pose a considerable risk of airborne virus infection for the workers in that environment. Within a chilled atmosphere workers could be at risk for relatively long periods as the virus will be more stable. The occurrence of a high incidence of infection in meat factories within a short period is, I believe, more likely to be the result of workers being infected from the same source simultaneously rather than by person-to-person transmission. In the latter case, the occurrence of cases would have been more protracted due to the five- to 14-day incubation period, instead of the sharp ‘spikes’ that were seen. This is not to say, of course, that both mechanisms did not occur. There might have been a series of introductions, followed by infection from the contaminated environment, followed by person-to-person transmission. There is a need to provide better protection from airborne virus for workers within meat factories Finally, workers could be screened frequently to reduce the likelihood of those with coronavirus entering the facilities.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.725
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.173
GPT teacher head0.318
Teacher spread0.146 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2020
Admission routes1
Has abstractyes

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