Bibliographic record
Abstract
The Public Health Agency of Canada (the Agency) has an important role to play in collaboration with its provincial/territorial partners to advance preparedness for emerging and re-emerging high-consequence infectious diseases. During the 2014 Ebola outbreak, the Agency established Ebola Virus Disease (EVD) Rapid Response Teams that were available to any requesting provincial/territorial jurisdiction with a laboratory confirmed case of EVD. Working with provincial and territorial officials, a Rapid Response Team Concept of Operations was developed which outlined the process for Rapid Response Team engagement as well as the suite of technical expertise available. The Concept of Operations was refined further following a series of face-to-face advance planning meetings with individual provincial and territorial jurisdictions. This led to a consensus agreement that the Agency's Rapid Response Team should be available to support management of both confirmed and suspected EVD cases. There was also unanimous support from provincial and territorial jurisdictions that the concept and operationalization of the Agency's Rapid Response Team should be broadened to provide surge-capacity support to the provinces and territories to include any event with significant public health consequences. The Agency will continue to engage with domestic and international partners regarding best practices to maintain a highly skilled and nimble Rapid Response Team that is operationally ready to support both domestic and international public health emergencies.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.055 | 0.015 |
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".