Acknowledgements and Editorial (Exploring the Role of “Treatment as Prevention”)
Bibliographic record
Abstract
Free access to this special issue has been made possible by the financial support of the 1st Annual Treatment as Prevention (TasP) Workshop, held May 4-6, 2011 in Vancouver, British Columbia (BC), Canada. The Workshop was organised and hosted by the BC Centre for Excellence in HIV/AIDS and co-hosted by the International AIDS Society, the Joint United Nations Programme on HIV/AIDS (UNAIDS), the World Health Organization and the National Institute on Drug Abuse. Co-sponsors include the National Institutes of Health Office of AIDS Research, the National Institute of Allergy and Infectious Diseases, the United States President’s Emergency Plan for AIDS Relief (PEPFAR), the Agence Nationale de Recherche sur le SIDA et les hépatites virales (ANRS), the Bill & Melinda Gates Foundation, the Canadian Institutes for Health Research and the Public Health Agency of Canada. Academic partners included the University of British Columbia, Simon Fraser University, Vancouver Coastal Health Authority and Providence Health Care, Vancouver. Industry sponsors included Bristol- Myers Squibb, Boehringer Ingelheim, Gilead Sciences, Janssen, Merck, and ViiV Healthcare. Additional Industry supporters included Abbott Laboratories and Biolytical.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.030 | 0.025 |
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".