Additional file 8 of Immunoglobulin recognition of fecal bacteria in stunted and non-stunted children: findings from the Afribiota study
Bibliographic record
Abstract
Additional file 8: Fig. S8 Distribution of IgA-targeting by study metadata according to batch and country. (A) Summary of PERMANOVA analysis of the IgA Index in the full dataset when permutations were constrained by either sequencing batch (All ~Batch) or country (All ~Country) using the ‘strata’ parameter, or when analysis was performed in each batch individually (Batch1 and Batch2). Analysis is based on taxa maintained at the ASV level. (B) Summary of PERMANOVA analysis of the IgA Index in the full dataset and in each batch and country individually, based on taxa binned at the genus level. Starred variables are significant with an FDR-corrected p < 0.05. Each variable was tested individually in the PERMANOVA without other covariates. (C–F) IgA Index of taxa that different significantly by country in IgA-targeting. Statistical significance was determined by FDR-corrected linear models that incorporated sequencing depth, batch effect, age and sex. (G–J) Unsorted relative abundance of these same taxa. Numbers and letters following a genus indicate the first digits of the qiime2 feature code for a unique ASV. CAR, Central African Republic; Mada, Madacascar; Coef, the coefficient of variance by PERMANOVA; whz_cont, weight-for-height z-score; haz, height-for-age z-score; crp, serum c-reactive protein; aat, fecal alpha-1 antitrypsin. N = 138 total; N = 78 in Madagascar and N = 60 in CAR; N = 53 in Batch 1 and N = 85 in Batch 2.
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.805 | 0.081 |
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".