Learning From SARS in Hong Kong and Toronto
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
THE RECURRENCE OF SEVERE ACUTE RESPIRATORY SYNdrome (SARS) in China during 2004 has highlighted the continuing threat to human health from infectious disease outbreaks. A zoonosis caused by a novel coronavirus, SARS first emerged among humans in the southern Chinese province of Guangdong during November 2002. By March 2003, SARS had spread to neighboring Hong Kong and from there to Toronto, Ontario, and many other areas in a matter of days. The World Health Organization (WHO) has reported that by July 2003 when the epidemic had waned, in Hong Kong there were 1755 probable cases of SARS with 300 deaths (17%) and in Canada there were 251 probable cases with 43 deaths (17%). Most Canadian cases and all deaths were in the Toronto area. Both areas had serious difficulties managing the outbreak, and several inquiries into public health and epidemic management have since been performed. We led the panels that first reported on SARS and public health in each jurisdiction. Both panels worked through the summer of 2003 and issued their reports within a week of one another in early October 2003. Herein, we compare our findings, highlight common conclusions, and suggest some general lessons that may be applicable to other areas.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it