The Gut Microbiota–Brain Axis Expands Neurologic Function: A Nervous Rapport
Bibliographic record
Abstract
Does exploration of the gut microbiota-brain axis expand our understanding of what it means to be human? Recognition and conceptualization of a gut microbiota-brain axis challenges our study of the nervous system. Here, integrating gut microbiota-brain research into the metaorganism model is proposed. The metaorganism-an expanded, dynamic unit comprising the host and commensal organisms-asserts a radical blurring between man and microbe. The metaorganism nervous system interacts with the exterior world through microbial-colored lenses. Ongoing studies have reported that gut microbes contribute to brain function and pathologies, even shaping higher neurological functions. How will continued collaborative efforts (e.g., between neurobiology and microbiology), including partnerships with the arts (e.g., philosophy), contribute to the knowledge of microbe-to-mind interactions? While this is not a systemic review, this nascent field is briefly described, highlighting ongoing challenges and recommendations for emerging gut microbiota-brain research. Also see the video abstract here https://youtu.be/lP9gOW8StXg.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".