H<sub>2</sub>Oh No! The importance of reporting your water source in your <i>in vivo</i> microbiome studies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it