Did Lessons From SARS Help Canada’s Response to COVID-19?
Bibliographic record
Abstract
Did Lessons From SARS Help Canada's Response to COVID-19? Michael Silverman MD, Michael Clarke PhD, and Saverio Stranges MD, PhD Affiliation Michael Silverman is with the Department of Medicine, Division of Infectious Diseases, and the Department of Epidemiology and Biostatistics, Schulich School of Medicine & Dentistry, Western University, London, Ontario, Canada. Michael Clarke is with the Interfaculty Program in Public Health, Western University. Saverio Stranges is with the Department of Epidemiology and Biostatistics and the Department of Family Medicine, Schulich School of Medicine & Dentistry, Western University, and the Department of Population Health, Luxembourg Institute of Health, Strassen, Luxembourg.CopyRightCorrespondence should be sent to Michael Silverman, MD, Chair of Division of Infectious Diseases, St. Joseph's Health Care, Room B3 404, 268 Grosvenor St, London, Ontario, Canada, N6A 4V2 (e-mail: michael.silverman@sjhc.london.on.ca). Reprints can be ordered at http://www.ajph.org by clicking the "Reprints" link.CONTRIBUTORSM. Silverman wrote a first draft of the editorial. M. Clarke and S. Stranges provided a critical review of the editorial and contributed to the writing of specific sections. https://doi.org/10.2105/AJPH.2020.305936 Accepted: August 16, 2020 Published Online: November 12, 2020
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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 | 0.016 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.014 | 0.023 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".