Bibliographic record
Abstract
The 9th Canadian Immunization Conference was held on 5-8 December 2010 in Quebec City, Canada. Over 1000 academic, public health and vaccine industry scientists, nurses, pharmacists, physicians and policy makers attended the conference, which was organized by the Public Health Agency of Canada-Centre for Immunization Research and Respiratory Infectious Diseases in collaboration with the Canadian Association of Immunization Research and Evaluation, the Canadian Paediatric Society and the Canadian Public Health Association. Fresh from the pandemic influenza A H1N1 2009-2010 experience, in which Canada experienced a smaller Spring 2009 wave followed by a Fall wave that stretched public health prevention and healthcare system resources, conference attendees were given the chance to reflect on lessons from the perspective of communication strategies, vaccine effectiveness, safety and program delivery techniques, in one of six program streams devoted to H1N1. The five other streams were immunization in a global community, vaccine safety, new technologies, vaccine-specific issues and clinical practice. In this article, we summarize some of the key presentations from the six plenary sessions, 36 concurrent symposia and workshops, podium and poster presentations.
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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".