Additional file 2 of Gut microbiome signatures linked to HIV-1 reservoir size and viremia control
Bibliographic record
Abstract
Additional file 1: Supplementary methods. Supplementary results. Figure S1. BCN02-microbiome study design and sample collection strategy. Figure S2. Overview of sample disposition for multi-omic analysis. Figure S3. Taxonomic classification of fecal samples at the order level. Figure S4. Differential abundance in Methanobacteriales between controllers and non-controllers. Figure S5. Linear Discriminant Analysis (LDA) effect size (LEfSe) at the species level. Figure S6. Longitudinal feature-volatility analysis of Bacteroidales and Clostridiales species. Figure S7. Bray-Curtis dissimilarity index between controllers and non-controllers. Figure S8. Gut microbiome profiling excluding B07 participant from non-controllers arm. Figure S9. Differentially abundant metabolic pathways from Bacteroidales and Clostridiales at the study entry. Figure S10. Differential metabolic pathways between controllers and non-controllers. Figure S11. Longitudinal variation of differentially abundant pathways over the trial. Figure S12. Differential archaeal metabolic pathways between controllers and non-controllers. Figure S13. Spearman’s correlation between clinical data, vaccine response and gut microbial variables. Figure S14. Differentially expressed PBMC host genes between controllers and non-controllers at baseline. Figure S15. Protein inflammation markers from controllers and non-controllers at baseline. Figure S16. Functional enrichment of transcripts correlated with the ratio Bacteroidales:Clostridiales and viral reservoir. Figure S17. Integrated analysis of microbiome, metaproteome and transcriptome data.
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 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.869 | 0.127 |
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".