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Record W4391536006 · doi:10.1177/25148486241229012

Ventilation shutdown and the breath-taking violence of infectious disease emergency management in industrial livestock production

2024· article· en· W4391536006 on OpenAlexaff
Martin Sinel, Tony Weis

Bibliographic record

VenueEnvironment and Planning E Nature and Space · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsWestern University
Fundersnot available
KeywordsContext (archaeology)Infectious disease (medical specialty)BiosecurityBusinessLivestockProduction (economics)Natural resource economicsEnvironmental planningDiseaseBiologyGeographyMedicineEcologyEconomics

Abstract

fetched live from OpenAlex

Powerful ventilation systems are a crucial technology in industrial livestock production, mitigating the unhealthy ambient conditions that result from great densities of animal bodies, biowastes, and chemical agents, and enabling the rapid production of massive quantities of flesh and eggs in such crowded indoor spaces. But bad air is just one of many biophysical and technoscientific challenges managed in these spaces, chief among them the ever-present risks of infectious disease transmission and evolution that threaten animal health and productivity and pose untold risks for humans. This article examines the intersection of these two central problems, where ventilation systems that are normally used to manage bad air within enclosures have been repurposed in the context of disease outbreaks to quickly and cheaply kill infected populations by hyperthermia. An analysis of this nascent practice, euphemistically termed “ventilation shutdown,” shows how governments and publicly funded scientific institutions have worked with private industry to develop and systematize the use of this and other technologies of mass death to respond to infectious disease emergencies, a dynamic that, we argue, sheds new light on both the precarity and the violence of industrial livestock production.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score0.132

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.236
Teacher spread0.219 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations9
Published2024
Admission routes1
Has abstractyes

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