Additional file 1 of Divergent maturational patterns of the infant bacterial and fungal gut microbiome in the first year of life are associated with inter-kingdom community dynamics and infant nutrition
Bibliographic record
Abstract
Additional file 1: Table S1. 16S and ITS2 read counts before and after sequence processing with the DADA2 pipeline (related to Figure S1). Table S2. Differences in CLR-transformed abundance of the top 15 bacterial genera by infant age and bacterial alpha diversity trend (related to Fig. 3A-B and Figure S4). Table S3. Differences in CLR-transformed abundance of the top 15 fungal genera by infant age and fungal alpha diversity trend (related to Fig. 3D-E and Figure S5). Table S4. Pair-wise comparison of typical (inverse), bacteria atypical, and fungi atypical inter-kingdom microbial co-occurrence network properties at 3 and 12 months (related to Fig. 4). Table S5. Bacterial co-occurrence network properties between typical and atypical alpha diversity trends at 3 and 12 months (related to Figure S6). Table S6. Fungal co-occurrence network properties between typical and atypical alpha diversity trends at 3 and 12 months (related to Figure S7). Table S7. Inter-kingdom co-occurrence network properties between typical and atypical (bacteria, fungi, or both) alpha diversity trends at 3 and 12 months (related to Figure S8). Table S8. Logistic regression statistics between maternal, infant, and early-life factors and bacterial alpha diversity trend (related to Fig. 4B). Table S9. Logistic regression statistics between maternal, infant, and early-life factors and fungal alpha diversity trend (related to Fig. 4D). Figure S1. 16S and ITS2 sequencing depth and sample composition (related to Table S1). Figure S2. Divergent bacterial richness maturation patterns are observed in the first year of life (related to Fig. 1). Figure S3. Divergent fungal richness maturation patterns are observed in the first year of life (related to Fig. 2). Figure S4. Individual-level taxonomic differences between infants with an increasing vs. decreasing bacterial alpha diversity trend at 3 and 12 months (related to Fig. 3 and Table S2). Figure S5. Individual-level taxonomic differences between infants with a decreasing vs. increasing fungal alpha diversity trend at 3 and 12 months (related to Fig. 3 and Table S3). Figure S6. Differences in bacterial co-occurrence networks are observed between increasing and decreasing alpha diversity trends at 3 and 12 months (related to Fig. 4 and Table S5). Figure S7. Differences in fungal co-occurrence networks are observed between increasing and decreasing alpha diversity trends at 3 and 12 months (related to Fig. 4 and Table S6). Figure S8. Differences in inter-kingdom co-occurrence networks are observed between infants with a typical (inverse) bacterial and fungal alpha diversity trend and atypical changes in bacterial, fungal, or both alpha diversity trends at 3 and 12 months (related to Fig. 4 and Table S7).
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.862 | 0.128 |
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".