Additional file 2 of Exploratory analysis of the potential impact of violence on HIV among female sex workers in Mombasa, Kenya: a mathematical modelling study
Bibliographic record
Abstract
Additional file 2: Text S7-8; Figures S3-S20. Tables S5-S10. Text S7 – Model calibration and cross-validation. Text S8 – Additional results. Figures S3 and S4 – Model calibration and cross-validation to ART coverage. Figure S5 – Model cross-validation to condom use. Figure S6 – Model cross-validation to violence exposure. Figure S7 – Modelled prevalence of violence. Figure S8 – Modelled incidence of violence. Figure S9 – modelled percentage of ART-naïve people living with HIV. Figure S10 – comparison of ART coverage between scenarios. Figure S11- comparison of condom use between scenarios. Figure S12 and S13 – tPAF in FSWs and clients. Figure S14 – Prevalence of violence over time with/without intervention. Figure S15 and S16– impact of violence intervention for FSWs and clients. Figure S17 and S18 – scatter plots showing correlation between 10 year tPAF/10 year intervention impact and corresponding parameter values. Figures S19 and S20 – Sensitivity analysis results. Table S5 – HIV prevalence cross-validation data. Table S6 – ART coverage cross-validation data. Table S7 – condom use cross-validation data. Table S8 – Older FSW violence cross-validation data.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.685 | 0.047 |
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".