Additional file 1 of Biological observations in microbiota analysis are robust to the choice of 16S rRNA gene sequencing processing algorithm: case study on human milk microbiota
Bibliographic record
Abstract
Additional file 1: Table S1. Comparison of the number of OTUs/ASVs in the mock community, negative controls, and milk microbiota datasets processed by clustering-based OTU method (Qiime1) and a denoising algorithm (Qiime2) with or without contaminant removal. Table S3. Comparison of prevalence and relative abundance of shared genera in milk microbiota processed by clustering-based OTU method (Qiime1) and a denoising algorithm (Qiime2) without contaminant removal. Figure S1. Comparison of the library size on datasets processed by clustering-based OTU method (Qiime1) and a denoising algorithm (Qiime2) with or without contaminant removal. Figure S2. Comparison of the prevalence of bacterial genera on datasets processed by clustering-based OTU method (Qiime1) and a denoising algorithm (Qiime2) without contaminant removal. Figure S3. Distribution of classified genera in Qiime1 and Qiime2 processed datasets without contaminant removal. Figure S4. Comparison of the relative abundance and prevalence of abundant bacterial genera (> 0.01% mean relative abundance) on datasets processed by clustering-based OTU method (Qiime1) and a denoising algorithm (Qiime2) without contaminant removal. Figure S5. Comparison of the composition of abundant taxa (> 1% mean relative abundance) on datasets processed by clustering-based OTU method (Qiime1) and a denoising algorithm (Qiime2) with or without contaminant removal.
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 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.747 | 0.117 |
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".