Bibliographic record
Abstract
Premodern European Animal Plagues 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.This article is a step in that direction.
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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".