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Record W4389301212 · doi:10.7788/9783412525729.235

Premodern European Animal Plagues. Common but Enigmatic?

2023· book-chapter· en· W4389301212 on OpenAlexaff
Timothy P. Newfield

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Diversity and Health Studies
Canadian institutionsSocial Sciences and Humanities Research Council
Fundersnot available
KeywordsLicenseDownloadLibrary scienceAttributionPolitical scienceWorld Wide WebComputer scienceLawPsychology

Abstract

fetched live from OpenAlex

Common but Enigmatic?The history of non-human animal plagues in premodernity very much remains in its infancy. 1 While our understanding of premodern animal disease has advanced considerably in recent years, specifically in regards to the fourteenth-and eighteenth-century European bovine panzootics, much remains unknown about the disease outbreaks domesticated and undomesticated animals suffered centuries and millennia ago across all world regions.This article seeks to survey the state of our knowledge about animal plagues in western Eurasia, focusing on the Middle Ages.It employs case studies of animal plagues observed in the sixth, early-ninth, late-tenth and early-fourteenth centuries to probe the limits of what we can glean about premodern animal plagues from written sources alone, to tease out the relevance of recent and ongoing work in the paleo-and phylo-genetic sciences, and to advance a framework for studying animal disease outbreaks in the distant past.There is much work to do and no one discipline or scholar can do it alone.To contextual recent histories of epizootic and zoonotic disease, to begin to establish trends in outbreaks over time and space, and to start to identify triggers of, and risk factors for, historical animal disease outbreaks, we must advance a new agenda, one that seeks to interdisciplinarily interrogate the diverse evidence we have for past animal disease and to establish what data we might look to produce.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.498
Threshold uncertainty score0.999

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)0.0020.005

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 head0.089
GPT teacher head0.231
Teacher spread0.141 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2023
Admission routes1
Has abstractyes

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