Gut Microbiome Standardization in Control and Experimental Mice
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
Mouse models are used extensively to study human health and to investigate the mechanisms underlying human disease. In the past, most animal studies were performed without taking into consideration the impact of the microbiota. However, the microbiota that colonizes all body surfaces, including the gastrointestinal tract, respiratory tract, genitourinary tract, and skin, heavily impacts nearly every aspect of host physiology. When performing studies utilizing mouse models it is critical to understand that the microbiome is heavily impacted by environmental factors, including (but not limited to) food, bedding, caging, and temperature. In addition, stochastic changes in the microbiota can occur over time that also play a role in shaping microbial composition. These factors lead to massive variability in the composition of the microbiota between animal facilities and research institutions, and even within a single facility. Lack of experimental reproducibility between research groups has highlighted the necessity for rigorously controlled experimental designs in order to standardize the microbiota between control and experimental animals. Well controlled experiments are mandatory in order to reduce variability and allow correct interpretation of experimental results, not just of host-microbiome studies but of all mouse models of human disease. The protocols presented are aimed to design experiments that control the microbiota composition between different genetic strains of experimental mice within an animal unit. © 2017 by John Wiley & Sons, Inc.
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 | 0.000 | 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