Biodiversity, the Human Microbiome and Mental Health: Moving toward a New Clinical Ecology for the 21st Century?
Bibliographic record
Abstract
Advances in research concerning the brain-related influences of the microbiome have been paradigm shifting, although at an early stage, clinical research involving beneficial microbes lends credence to the notion that the microbiome may be an important target in supporting mental health (defined here along the continuum between quality of life and the criteria for specific disorders). Through metagenomics, proteomics, metabolomics, and systems biology, a new emphasis to personalized medicine is on the horizon. Humans can now be viewed as multispecies organisms operating within an ecological theatre; it is important that clinicians increasingly see their patients in this context. Historically marginalized ecological aspects of health are destined to become an important consideration in the new frontiers of practicing medicine with the microbiome in mind. Emerging evidence indicates that macrobiodiversity in the external environment can influence mental well-being. Local biodiversity may also drive differences in human-associated microbiota; microbial diversity as a product of external biodiversity may have far-reaching effects on immune function and mood. With a focus on the microbiome as it pertains to mental health, we define environmental “grey space” and emphasize a new frontier involving bio-eco-psychological medicine. Within this concept the ecological terrain can link dysbiotic lifestyles and biodiversity on the grand scale to the local human-associated microbial ecosystems that might otherwise seem far removed from one another.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".