Bibliographic record
Abstract
Changes in gut microbiota can modulate the peripheral and central nervous systems, resulting in altered brain functioning, and suggesting the existence of a microbiota gut-brain axis. Diet can also change the profile of gut microbiota and, thereby, behavior. Effects of bacteria on the nervous system cannot be disassociated from effects on the immune system since the two are in constant bidirectional communication. While the composition of the gut microbiota varies greatly among individuals, alterations to the balance and content of common gut microbes may affect the production of molecules such as neurotransmitters, e.g., gamma amino butyric acid, and the products of fermentation, e.g., the short chain fatty acids butyrate, propionate, and acetate. Short chain fatty acids, which are pleomorphic, especially butyrate, positively influence host metabolism by promoting glucose and energy homeostasis, regulating immune responses and epithelial cell growth, and promoting the functioning of the central and peripheral nervous systems. In the future, the composition, diversity, and function of specific probiotics, coupled with similar, more detailed knowledge about gut microbiota, will potentially help in developing more effective diet- and drug-based therapies.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.002 |
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".