H<sub>2</sub>Oh No! The importance of reporting your water source in your <i>in vivo</i> microbiome studies
Bibliographic record
Abstract
microbiome experiment however, it is also one of the most overlooked and underreported variables within the literature. Currently there is no established standard for drinking water quality set by the Canadian Council on Animal Care. Most water treatment methods focus on inhibiting bacterial growth within the water while prolonging the shelf-life of bottles once poured. When reviewing the literature, it is clear that some water treatment methods, such as water acidification, alter the gut microbiome of experimental animals resulting in dramatic differences in disease phenotype progression. Furthermore, The Jackson Lab, one of the world's leading animal vendors, provides acidified water to their in-house animals and is often cited in the literature as having a dramatically different gut microbiome than animals acquired from either Charles River or Taconic. While we recognize that it is impossible to standardize water across all animal facilities currently conducting microbiome research, we hope that by drawing attention to the issue in this commentary, researchers will consider water source as an experimental variable and report their own water sources to facilitate experimental reproducibility. Moreover, researchers should be cognisant of potential phenotypic differences observed between commercial animal vendors due to changes in the gut microbiome as a result of various sources of water used.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".