Cross-Study Analyses of Gut Microbiomes from Healthy and Obese Individuals
Bibliographic record
Abstract
Abstract Background: With the advent of metagenomics, many large studies have been conducted with the quest of better understanding gut microbiota changes in relation to varying health conditions. Significant findings have been made for diseases such as cirrhosis, colorectal cancers, inflammatory bowel diseases and others, yet one that stands out is obesity for which conflicting results have been reported in the literature. Methods: Here, we built and analyzed a cross-study dataset of healthy and obese individuals looking for major changes in the the taxonomic and functional composition of their metagenomes. Results: Our results suggest that the overweight and normal subjects have no strong dissimilarity in their metagenomes composition. Significant differences were observed when comparing the obese and the non-obese individuals in their functional and taxonomic profiles. Conclusion: In this study, we report the most significant changes that we observed and discuss their potential implication in the obesity condition.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".