Bibliographic record
Abstract
A whole food plant-based (WFPB) diet is generally defined as a diet rich in fruits, vegetables, whole grains, legumes, nuts and seeds, and herbs and spices. Many define a WFPB diet as being exclusively plant-based with no animal products, excluding all red meat, poultry, fish, eggs, and dairy products. Other sources define it as a plant-forward dietary pattern that may still include small amounts of meat, eggs or dairy. A WFPB dietary pattern focuses on unprocessed plant foods, while avoiding processed foods containing refined grains, refined oils and added sugars. Figure 1 depicts an example of a balanced WFPB meal. On a practical level, it is important to distinguish a WFPB diet from a vegan diet, which eliminates all animal products but may include processed vegan foods (e.g., plant-based meats, pastries and fried foods). However, in the scientific literature, the term “vegan” is often used, and at times it is difficult to assess the amount of processed food included in diets of vegans included in observational studies. This paper will focus primarily on the evidence for an exclusively WFPB dietary pattern in the prevention and management of Type 2 diabetes mellitus (T2DM) and obesity. However, given certain limitations in the literature, some data on vegan diets and plant-rich but not exclusively plant-based diets will also be included.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".