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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.008 |
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; both teacher heads agree on what is shown here.
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".