Canadian Adjuvant Initiative Workshop, March 26–27, 2013—Ottawa, Canada
Bibliographic record
Abstract
Novel adjuvants hold the promise for developing effective modern subunit vaccines capable of appropriately modulating the immune response against challenging diseases such as those caused by chronic and/or intracellular pathogens and cancer. Over the past decade there has been intensive research into discovering new adjuvants, however, their translation into routine clinical use is lagging. To stimulate discussion and identify opportunities for networking and collaboration among various stakeholders, a Canadian Adjuvant Initiative Workshop was held in Ottawa. Sponsored by the National Research Council Canada, Canadian Institutes of Health Research and the Vaccine Industry Committee, a two day workshop was held that brought together key Canadian and international stakeholders in adjuvant research from industry, academia and government. To discover innovation gaps and unmet needs, the presentations covered a board range of topics in adjuvant development; criteria for selection of lead adjuvant candidates from an industry perspective, discovery research across Canada, bioprocessing needs and challenges, veterinary vaccines, Canadian vaccine trial capabilities, the Canadian regulatory framework and WHO formulation laboratory experience. The workshop concluded with a discussion on the opportunity to create a Canadian Adjuvant Development Network. This report details the key discussion points and steps forward identified for facilitating adjuvant development research in Canada.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.084 | 0.012 |
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".