RESEARCH Sex-for-Crack exchanges: h
Bibliographic record
Abstract
to elevated rates for sexually transmitted infections (STIs), including HIV transmission [1-3], through in-ported a 3.87 increased odds of acquiring HCV among participants who used crack [1,6]. In addition to the sex-Duff et al. Harm Reduction Journal 2013, 10:29 http://www.harmreductionjournal.com/content/10/1/29women’s crack use may exceed men’s [9-11]. For example,East Mall, Vancouver, BC V6T 1Z3, CANADA Full list of author information is available at the end of the articlecreased sexual risk pathways (e.g. higher number of sex-ual partners and unprotected sex) [4,5]. Crack cocaine use has been documented as a predictor for both HIV and HCV, even after adjusting for known confounders such as injection drug use, suggesting a non-parenteral ual and drug risk pathways, the use of non-injection crack cocaine has been linked to an array of adverse physical and mental health outcomes, including elevated individual and community-level violence and physical health harms, such as oral sores and pulmonary compli-cations [5,7,8]. While the population prevalence of crack use varies across settings, a growing number of studies in high-income settings have suggested that street-involved
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.118 | 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".