Bibliographic record
Abstract
The increasing prevalence of obesity is becoming a global health concern due to its association with chronic diseases including type 2 diabetes, non-alcoholic fatty liver disease, and cardiovascular diseases. Obesity occurs when energy intake outweighs energy expenditure, leading to a conventional intervention strategy being “eat less and move more.” However, this strategy does not consider the influence of genetic factors and their interactions with environmental factors (diets and physical activity), making obesity prevention and management inefficient. To better understand obesity, research in nutrigenetics and nutrigenomics seek to explore the influence of genetic variations on dietary responses, and how dietary components alter gene expression in obese individuals. Current evidence suggests that variations in genes involved in lipid regulation, carbohydrate metabolism, and energy homeostasis are strongly associated with the risk of obesity and its related metabolic syndromes. In addition, diet-gene interactions influence intervention effectiveness for obesity management. By examining obesity-related metabolic pathways, we can reveal the functional basis of diet-gene interactions in relation to obesity risk. Although limitations exist within the current literature, emerging evidence indicates that obesity risk and intervention can be affected by diet-gene interactions, and continued research is needed for further exploration.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".