Bibliographic record
Abstract
We didn’t ask for it. We didn’t expect it. YetCanada has run the first leg of a race againstSARS, and there are reasons to take courage. Canada was forced into this race on Mar. 13, 2003, as the first country outside Asia to recognize SARS cases. The very first cases had been diagnosed in Hong Kong only a few days earlier. Credit for early recognition of the problem should be shared. Preparations in Canada to con-front the threat of a pandemic of influenza had been under way for 2 years. It was against a background of such plan-ning that concern about a small cluster of human cases of avian influenza in Hong Kong had led Health Canada to is-sue an alert on Feb. 19. This alert underscored the poten-tial for global spread and urged laboratories and public health practitioners to be vigilant. Alerts issued Feb. 20 and 24 and again on Mar. 12 by the British Columbia Centre for Disease Control (BCCDC) noted both avian influenza and a mysterious outbreak of atypical pneumonia in Guangdong Province in southern China. These alerts for BC clinicians, infection control practitioners and public health authorities called for enhanced surveillance and for infection control measures with respect to patients present-ing with unusual influenza-like illness after returning from Hong Kong or China. It is likely that these alerts played a role in the decision by physicians at Vancouver General Hospital to institute early isolation of a 55-year-old man who presented to the emergency department on Mar. 7 with a history of recent travel from Hong Kong and symptoms of pneumonia. They certainly contributed to a report being telephoned to BCCDC when the patient’s condition worsened on Mar. 13. This report, together with timely conversations be-tween Dr. Danuta Skowronski (BCCDC), Dr. Allison
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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 | 0.008 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.021 | 0.017 |
| Insufficient payload (model declined to judge) | 0.197 | 0.065 |
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".