Microbiome profiling of drinking water in relation to incidence of inflammatory bowel disease
Bibliographic record
Abstract
The etiology of inflammatory bowel disease (IBD) is unknown; current research is focused on determining environmental factors. One consideration is drinking water: water systems harbour considerable microbial diversity, with bacterial concentrations estimated at 10(6)-10(8) cells/L. Perhaps differences in microbial ecology of water sources may impact differential incidence rates of IBD. Regions of Manitoba were geographically mapped according to incidence rates of IBD and identified as high (HIA) or low (LIA) incidence areas. Bulk water, filter material, and pipe wall samples were collected from public buildings in different jurisdictions and their population structure analyzed using 16S rDNA sequencing. At the phylum level, Proteobacteria were observed significantly less frequently (P = 0.02) in HIA versus LIA. The abundance of Proteobacteria was also found to vary according to water treatment distribution networks. Gammaproteobacteria was the most abundant class of bacteria and was observed more frequently (P = 0.006) in LIA. At the genus level, microbes found to associate with HIA include Bradyrhizobium (P = 0.02) and Pseudomonas (P = 0.02). Particular microbes were found to associate with LIA or HIA, based on sample location and (or) type. This work lays out a basis for further studies exploring water as a potential environmental source for IBD triggers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".