The other human immunodeficiency viruses
Bibliographic record
Abstract
Although they contribute little to the overall burden of AIDS in the world, the other HIVs (HIV-1 groups O, N and P, and HIV-2) can provide useful insight into the events that led to the emergence of pandemic HIV-1 group M. How was it possible for HIV-2, a different virus that originated from a different simian host, to spread in a different region of Africa at roughly the same time (give or take a few decades) as HIV-1, only to disappear quietly thereafter? And why was HIV-1 group M so successful compared to the others? HIV-1 groups O, N and P Highly divergent strains of HIV-1 were described in the 1990s. The first, now known as HIV-1 group O (‘O’ for outlier), has only 50–65% homology in nucleotide sequences compared to HIV-1 group M, which is why it is considered as a different ‘group’ rather than a different ‘subtype’ (subtypes differ by about 20%; in other words, they have 80% homology). The original isolates of HIV-1 group O had been obtained from two Cameroonians living in Belgium, a young woman and her husband. Additional cases were documented among Cameroonians living in France, and in Cameroon itself. Further studies confirmed that Cameroon was the epicentre of HIV-1 group O, where it accounted for 2% of all HIV-1 infections, versus 1% in adjacent Gabon and Nigeria. A few cases were found in other African countries. Within Cameroon, regional variations were noted, with group O representing 6% of all HIV-1 positive sera in Yaoundé but only 1% in northern provinces. When stored sera were tested, group O represented 21% of all HIV-1 positive sera in 1986–8, 9% in 1989–91, 3% in 1994–5 and only 1% in 1997–8. It then remained rather stable at 1–2%.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.015 |
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".