Proceedings of the Canadian Association for HIV Research: Canadian Foundation for Infectious Diseases Professional Development Workshop for Viral Researchers
Bibliographic record
Abstract
In March 2018, the Canadian Association for HIV Research (CAHR) and Canadian Foundation for Infectious Diseases (CFID) collaborated to conduct a workshop targeted to mid-career virology researchers. Key objectives of the workshop included 1) sharing knowledge and expertise cutting across various viral diseases, 2) developing collaborations as we anticipate the next wave of suppressive and curative treatment for HIV, HBV, CMV, and other viral diseases, and 3) providing insights, advice, and "food for thought" as participants advance to mid- and later phases of their research careers. This article reports on the key topics contemplated including scientific misinformation within the public realm, network building, interdisciplinary collaboration, mentorship, and communicating with decision makers. Given the focus on virology, the Canadian Society for Virology was invited to highlight their efforts to build a cohesive network that is impactful in facilitating viral research in Canada including advocating for appropriate levels of peer-reviewed research funding. Many key pearls of wisdom are contained within this document which are of value to all researchers aiming for success in a continually evolving, complex, and challenging Canadian research and academic environment.
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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".