Bibliographic record
Abstract
In the past 20 years, research on the human gut microbiome and human health has exploded. Diet and gut microbiota are considered important parts of human health. Therefore, it is important and urgent to dig deeper into the impacts of different diets on the human gut and human health. This paper mainly compared the impacts of plant-based diets and animal-based high-fat low-fiber western diets on gut health and human diseases. Through introducing vegan, vegetarian, and mediterranean diets and related research, plant-based diets are much healthier than high-fat low-fiber western diets when it comes to fighting cancer and maintaining a healthy weight. The paper also focuses on the components and their effects in plant-based food and animal-based foods such as plant protein, animal protein, prebiotics, probiotics, and dietary iron such as heme as well as mentioning the effect of shifting diets. In the future, research can look for more evidence of people who change diets, such as changing from omnivorous to vegetarian, because nowadays more people change diets based due to recognition of the bad effect of western diets. However, those who switch diets may suffer from eating disorders so future research could look into this effect. Overall, this paper gives basic knowledge about the effect of different diets on human gut health and human health.
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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".