Bibliographic record
Abstract
The human gut microbiota presents a strong influence on health and disease development. Metagenomic analysis has revealed the importance of the interaction between the genomes of food, gut microbiota and the host. Also, the establishment of humanized mouse gut microbiota in appropriate animal models has further contributed to the understanding of its function. The composition of the gut microbiota presents a significant impact on the risk of disease development supported by findings of substantial individual variations. Many low molecular weight bacterial substances have been indicated to affect chromatin remodeling, regulation of apoptosis, cellular differentiation and inflammation. The gut microbiota has also been linked to the etiology of cancer because of how it can alter dietary exposures. Furthermore, microbial metabolites have been associated with epigenetic modifications, reversible heritable changes in gene expression without alterations in the primary DNA sequence, which may influence the risk of various cancers and other diseases. As many microbial metabolites are absorbed into systemic circulation, gene expression might also be affected in distal regions of the gut. Therefore, the interaction of dietary intake, gut microbiota and epigenetic modifications plays an important role in disease risk, development and prevention.
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.
How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".