Avian influenza the current state of affairs : the public health perspective in Canada / by Yvonne B. Kangong.
Bibliographic record
Abstract
In light of Canada?s need to deal with respiratory infectious diseases such as \ntuberculosis, Avian Influenza and severe acute respiratory syndrome (SARS), public \nhealth departments across the country have been challenged with the need to develop \naction plans for both treatment and prevention. Yet the problem is not isolated to Canada \nbut is considered to be a major public health threat both in Canada and internationally. \nThe emergence of viral respiratory infections in Canada may be attributed to several \nsources, which include migration from one part of the world to another either for \npleasure or business, as a major contributor. Such migrations coupled with inadequate \ninfectious disease prevention strategies not only leads to rapid transmission of respiratory \ninfectious diseases, but there is also a problem in administering effective treatment and \nvaccines to infected cohorts.Several factors contribute to both the accidental and/or \ndeliberate transfer of microbial agents. For example, economic, cultural and political \ninteractions invoke the emergence of new and unrecognized microbial disease agents \n(Lashley, 2006). New diseases have the potential to spread across the world in a matter of \ndays, or even hours, making early detection and action more important than ever \n(BCCDC, 2008).
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.033 | 0.008 |
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".