Bibliographic record
Abstract
Starting in late September 1872, horses started falling ill with a severe respiratory complaint in the countryside about a dozen miles north of Toronto, Ontario. Veterinary experts swiftly diagnosed the malady, which paralyzed street transportation, commerce, and everyday life in Toronto itself during the first weeks of October, as influenza. Over the next year, an equine plague that most contemporaries referred to as the epizootic—and which I call the Great Horse Flu in the book I am completing on this outbreak—spread throughout southern Canada, every reach of the United States, and parts of Cuba, Mexico, and Central America. The novel influenza virus responsible for this outbreak sickened between ninety and ninety-nine percent of horses, donkeys, and mules across this vast swath of the northern Americas.1 Our best guess is that the Great Horse Flu killed between one and four percent of the equines it afflicted—a case fatality rate roughly not unlike those recorded by the Great Influenza Pandemic of 1918–1920 and the COVID Pandemic. In less than a year, an estimated 112,500 to 554,000 horses and ponies perished alongside tens or hundreds of thousands of mules and donkeys.2
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".