Bibliographic record
Abstract
American Journal of Public Health 1387 risk of infection with hepatitis B and C viruses. Am J Epidemiol. 1999;149:203–213. 6. Normand J, Vlahov D, Moses LE, eds. Preventing HIV Transmission: The Role of Sterile Needles and Bleach. Washington, DC: National Academy Press; 1995. 7. Lurie P, Reingold AL, Bowser B, et al. The Public Health Impact of Needle Exchange Programs in the United States and Abroad. Vol 1. Atlanta, Ga: Centers for Disease Control and Prevention; 1993. 8. Schechter MT, Strathdee SA, Cornelisse PGA, et al. Do needle exchange programs increase the spread of HIV among injection drug users: an investigation of the Vancouver outbreak. AIDS. 1999;13(6):F45–F51. 9. Moss AR, Hahn JA. Needle exchange—no help for hepatitis? Am J Epidemiol. 1999;149: 214–216. 10. Heimer R, Kaplan EH, Khoshnood K, Jariwala B, Cadman EC. Needle exchange decreases the prevalence of HIV-1 proviral DNA in returned syringes in New Haven, Connecticut. Am J Med. 1993;95:214–220. 11. Hahn JA, Vranizan K, MossAR. Who uses needle exchange? A study of injection drug users in treatment in San Francisco. J Acquir Immune Defic Syndr Hum Retrovirol. 1997;15:157–164. 12. Bourgois P, Bruneau J. Needle exchange and the politics of science: confronting Canada’s cocaine injection epidemic with participant observation. Med Anthropol. In press. 13. Musto DF. The American Disease: Origins of Narcotic Control. New York, NY: Oxford University Press; 1987. 14. Nelson KE, Celentano DD, Eiumtrakol S, et al. Changes in sexual behavior and a decline in HIV infection among young men in Thailand. N Engl J Med. 1996;335:297–303. 15. Wawer MJ, Sewankambo NK, Serwadda D, et al. Control of sexually transmitted diseases for AIDS prevention in Uganda: a randomized controlled trial. Lancet. 1999;353:525–535. 16. Grosskurth H, Mosha F, Todd J, et al. Impact of improved treatment of sexually transmitted diseases on HIV infection in rural Tanzania: randomized controlled trial. Lancet. 1995;346: 530–536.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
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".