Additional file 3 of Gut microbiome signatures linked to HIV-1 reservoir size and viremia control
Bibliographic record
Abstract
Additional file 2: Dataset S1. Longitudinal microbial composition (relative abundance) in viremic controllers and non-controllers analyzed by MetaPhlAn2.0. Dataset S2. Raw bacterial protein counts at baseline in viremic controllers and non-controllers analysed using Scaffold Q+ software v4.9.0. Dataset S3. Raw gene counts at baseline in viremic controllers and non-controllers estimated using RSEM v1.3.0. Dataset S4. Soluble factors (normalized protein expression units) from the Olink inflammation panel at baseline in viremic controllers and non-controllers. Dataset S5. List of transcripts significantly correlated with the Bacteroidales:Clostridiales ratio adjusted for multiple comparisons (spearman’s rho and corresponding q-value<0.05 are reported). Dataset S6. Spearman’s correlation (rho=0.5) between viral reservoir (CA HIV-1 RNA and HIV-1 DNA), bacterial species (Bacteroidales and Clostrdiales), transcripts correlated with the ratio Bacteroidales / Clostridiales and differentially abundant microbial proteins (p<0.025). Dataset S7. Benjamini–Hochberg multiple hypothesis correction for unadjusted p-values ≤ 0.05 (metagenomics and metaproteomics datasets).
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 | 0.001 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.392 | 0.064 |
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".