Insights from the fifth International One Health Congress, 2018, Saskatoon, Canada
Bibliographic record
Abstract
The recent outbreaks of diseases including Ebola reemergence in the Democratic Republic of Congo (DRC), Nipah virus outbreak in India, Lassa virus in Nigeria and the continued Influenza pandemic show that we cannot predict outbreaks, however, developing response plans could help alleviate the burden of diseases. Developing response plans involves strategies such as creating a repository of microbial agents as well as the implementation of the plans. In addition, zoonotic experts and policy makers should work together in order to succeed in fighting against any disease outbreaks. We should also not forget about the importance of vaccination because of the benefits it has brought to humankind preventing disease occurrence. Furthermore, the challenges that come with vaccination including vaccine delivery and vaccine uptake need to be overcome to make sure that this public health tool continues to be effective. Overcoming these vaccination challenges would play a significant role in decreasing the overuse of antimicrobials, hence avoiding resistance. The One Health Community (OHC) therefore has the responsibility to advocate for the use of vaccines and show that it has costs benefits; this strategy has the potential to fight against antimicrobial resistance.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.127 | 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".