Long-Duration Space Travel Support Must Consider Wider Influences to Conserve Microbiota Composition and Function
Bibliographic record
Abstract
The microbiota is important for immune modulation, nutrient acquisition, vitamin production, and other aspects for long-term human health. Isolated model organisms can lose microbial diversity over time and humans are likely the same. Decreasing microbial diversity and the subsequent loss of function may accelerate disease progression on Earth, and to an even greater degree in space. For this reason, maintaining a healthy microbiome during spaceflight has recently garnered consideration. Diet, lifestyle, and consumption of beneficial microbes can shape the microbiota, but the replenishment we attain from environmental exposure to microbes is important too. Probiotics, prebiotics, fermented foods, fecal microbiota transplantation (FMT), and other methods of microbiota modulation currently available may be of benefit for shorter trips, but may not be viable options to overcome the unique challenges faced in long-term space travel. Novel fermented food products with particular impact on gut health, immune modulation, and other space-targeted health outcomes are worthy of exploration. Further consideration of potential microbial replenishment to humans, including from environmental sources to maintain a healthy microbiome, may also be required.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".