SARS: a case study in emerging infections.
Bibliographic record
Abstract
1. Introduction 2. Environmental and social influences on emerging infectious diseases: past, present, and future 3. Evolutionary genetics and the emergence of SARS coronavirus 4. Influenza as a model system for studying the cross-species transfer and evolution of the SARS coronavirus 5. Management and prevention of SARS in China 6. Confronting SARS: A view from Hong Kong 7. The aetiology of SARS: Koch's postulates fulfilled 8. Laboratory Diagnosis of SARS 9. Animal origins of SARS coronavirus: possible links with the international trade in small carnivores 10. Epidemiology, transmission dynamics and control of SARS: the 2002-2003 epidemic 11. Dynamics of modern epidemics 12. The international response to the outbreak of SARS, 2003 13. The experience of the 2003 SARS outbreak as a traumatic stress among frontline healthcare workers in Toronto: lessons learned 14. Informed consent and public health 15. What have we learnt from SARS? References Index
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 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) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".