Human behavior, not race or geography, is the strongest predictor of microbial succession in the gut bacteriome of infants
Bibliographic record
Abstract
Colonization of the gastrointestinal tract with microorganisms during infancy represents a critical control point for shaping life-long immune-mediated disease susceptibility. Abnormal colonization or an imbalance of microbes, termed dysbiosis, is implicated in several diseases. Consequently, recent research has aimed at understanding ways to manipulate a dysbiotic microbiome during infancy to resemble a normal, healthy microbiome. However, one of the fundamental issues in microbiome research is characterizing what a "normal" infant microbiome is based on geography, ethnicity and cultural variations. This review provides a comprehensive account of what is currently known about the infant microbiome from a global context. In general, this review shows that the influence of cultural variations in feeding practices, delivery modes and hygiene are the biggest contributors to microbial variability. Despite geography or race, all humans have similar microbial succession during infancy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".