Additional file 1 of Cluster-specific associations between the gut microbiota and behavioral outcomes in preschool-aged children
Bibliographic record
Abstract
Additional file 1: Figure S1. Gut microbiota clustering using Dirichlet multinomial mixtures. Figure S2. Flowchart of data analysis approach. Figure S3. Differences in stool metabolic pathways between the four clusters. Figure S4. Differences in stool nucleotide levels between the four clusters. Figure S5. Behavioral Symptom scores between the four clusters. Figure S6. Number of microbial taxa that correlate with stool histidine to urocanate turnover. Figure S7. Relationship between Social Skills and urocanate metabolism for participants with low histidine-urocanate turnover rates. Figure S8. Levels of stool urocanate and histidine-urocanate turnover between children with “at risk” and “not at risk” scores for Social Skills and Developmental Social Disorders. Table S1. Sample characteristics of all combined participants. Table S2. PERMANOVA results for stool metabolome beta diversity. Individual independent effects are depicted. Table S3. ANCOVA Results for Adaptive Skill Scores While Correction for Gestational Age at Birth, Birth Weight, Ethnicity and Child Grain Intake. Table S4. ANCOVA Results for Developmental Social Disorder Scores While Correction for Gestational Age at Birth, Birth Weight, Ethnicity and Child Grain Intake. Table S5. ANCOVA Results for Social Skill Scores While Correction for Gestational Age at Birth, Birth Weight, Ethnicity and Child Grain Intake. Table S6. ANCOVA Results for Functional Communication Scores While Correction for Gestational Age at Birth, Birth Weight, Ethnicity and Child Grain Intake. Table S7. ANCOVA Results for Daily Living Scores While Correction for Gestational Age at Birth, Birth Weight, Ethnicity and Child Grain Intake.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.841 | 0.093 |
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".