Retrovirology BioMed Central Editorial
Bibliographic record
Abstract
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Whenever there is a sensational criminal case involving HIV transmission, the media cover it with far more gusto than they usually devote to scientific advances in the field. For example, a murder trial is now taking place in Canada involving a man who has been accused of sexually transmitting HIV to 11 different women, two of whom have died of their infections. Moreover, it is alleged that the accused perpetrator deliberately withheld from these women the fact that he was HIV-positive and that he refused to use a condom during intercourse. Notwithstanding that the suspect is possibly psychopathic and uncaring, or possibly of low intelligence and unable to assess the consequence of his actions, most people probably hope that he is convicted, sentenced, and imprisoned for his acts. Furthermore, most people probably wish for the criminal justice system to pursue these cases with vigour. In fact, however, people should understand that such legal action, and the willingness of the courts to hear these cases, will only weaken the global battle against HIV transmission. Editorial
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.340 | 0.223 |
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; the direct Gemma label and the distilled Codex classifier 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".