Linking the Gut Microbial Ecosystem with the Environment: Does Gut Health Depend on Where We Live?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Global comparisons reveal a decrease in gut microbiota diversity attributed to Western diets, lifestyle practices such as caesarian section, antibiotic use and formula-feeding of infants, and sanitation of the living environment. While gut microbial diversity is decreasing, the prevalence of chronic inflammatory diseases such as inflammatory bowel disease, diabetes, obesity, allergies and asthma is on the rise in Westernized societies. Since the immune system development is influenced by microbial components, early microbial colonization may be a key factor in determining disease susceptibility patterns later in life. Evidence indicates that the gut microbiota is vertically transmitted from the mother and this affects offspring immunity. However, the role of the external environment in gut microbiome and immune development is poorly understood. Studies show that growing up in microbe-rich environments, such as traditional farms, can have protective health effects on children. These health-effects may be ablated due to changes in the human lifestyle, diet, living environment and environmental biodiversity as a result of urbanization. Importantly, if early-life exposure to environmental microbes increases gut microbiota diversity by influencing patterns of gut microbial assembly, then soil biodiversity loss due to land-use changes such as urbanization could be a public health threat. Here, we summarize key questions in environmental health research and discuss some of the challenges that have hindered progress toward a better understanding of the role of the environment on gut microbiome development.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it