Bibliographic record
Abstract
Cacao, the quintessential ingredient in all true chocolate and cocoa products is a highly complex food source. The three most common applications of chocolate had been a) inducing weight in emaciated patients b) stimulating the nervous system and c) improving digestion and elimination. The specific effects of chocolate may be attributable to specific constituents such as flavonoids. Over the past decade, there has been increased interest in the potential health benefits related to dietary flavonoids and in particular flavanol consumption. There have been many observational studies to date that have reported a positive correlation between the consumption of dietary flavonoids including flavanols and a reduced risk for cardiovascular disease. The consumption of cocoa flavanol containing food products have been shown to improve endothelial function, insulin sensitivity, and reduce blood pressure. Regular consumption of flavanol containing chocolate bar with added PS (plant sterol) as part of a low-fat diet can significantly lower blood cholesterol levels and also reduce the systolic blood pressure without affecting the body's weight adversely supporting the concept that inclusion of these types of specially formulated foods into a balanced diet may help to support cardiovascular health. An added benefit being the various polyphenols present in chocolate, influence the numerous cytokines and enzyme systems in human body.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.005 |
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".