Gut Microbiome Standardization in Control and Experimental Mice
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.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 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".