Making waves: From tap to gut- exploring the impact of drinking water on gut microbiota
Bibliographic record
Abstract
• There is a paucity of data on direct influences of DW microbiota on the gut. • Mounting evidence suggests relationships between DW and the gut microbiome. • Data on DW should be included in cohort and animal studies going forward. Drinking water (DW) harbours diverse microbial species and chemical attributes. Water comprises the greatest portion of our daily diet, ingested both on its own and used in the preparation of food. DW is our major source of liquids, which is vital to maintaining homeostasis, and can also supply essential minerals. Limited evidence suggests that DW plays a role in shaping the gut microbiome, which implies that it may impact human health. Despite its significant contribution to diet, DW is often overlooked in studies examining dietary influences on the gut microbiota. This perspective explores our current understanding of the link between DW and the gut microbiota - an area of human microbiome science that has been surprisingly understudied. Existing studies reveal links between DW source, microbiota composition, and gut health, emphasizing the need for comprehensive investigations. Understanding the interplay between DW and gut microbiota holds potential for tailored interventions to enhance human health.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".