HIVR4P 2016, Partnering for Prevention: Conference Summary and Highlights
Bibliographic record
Abstract
HIV Research for Prevention: AIDS Vaccine, Microbicide, and ARV-based Prevention Science (HIVR4P) was built on a growing consensus that effective HIV prevention requires a combination of approaches and that understanding, analyzing, and debating the cross-cutting issues that impact prevention research are all essential to combat the global HIV/AIDS epidemic. To that end, the biennial HIVR4P conference is dedicated to all biomedical HIV prevention research approaches, including HIV vaccines, microbicides, pre-exposure prophylaxis, and treatment as prevention. The HIVR4P 2016 conference was held in Chicago, Illinois (USA), on October 17-21, and included more than 700 scientific presentations and 21 satellite sessions covering the latest and most promising advances across the HIV prevention research field. The theme "Partnering for Prevention" represented the conference's commitment to breaking down silos between research disciplines as well as between researchers, program developers, care providers, advocates, communities, and funders. Delegates spanning 42 countries attended the conference. One-third of those in attendance were early career investigators, which reflects a firm commitment to emerging researchers and ultimately to the goal of developing a sustainable scientific enterprise well into the future. This article presents a concise summary of highlights from the conference. For a more detailed account, one may find full abstracts, daily summaries, and webcasts on the conference website at hivr4p.org.
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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.116 | 0.056 |
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".