Modeling Scenarios for the End of AIDS
Bibliographic record
Abstract
At the end of 2012, 3 decades after the human immunodeficiency virus (HIV) was first identified, neither a cure nor a fully preventive vaccine was available. Despite multiple efforts, the epidemic remains an exceptional public health challenge. At the end of 2012, it was estimated that, globally, 35 million people were living with HIV, 2.3 million had become newly infected, and 1.6 million had died from AIDS-related causes. Despite substantial prevention efforts and increases in the number of individuals on highly active antiretroviral therapy (HAART), the epidemic burden continues to be high. Here, we provide a brief overview of the epidemiology of HIV transmission, the work that has been done to date regarding HIV modeling in different settings around the world, and how to finance the response to the HIV epidemic. In addition, we suggest discussion topics on how to move forward with the prevention agenda and highlight the role of treatment as prevention (TasP) in curbing the epidemic.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".