Fatty acid composition and possible health effects of coconut constituents.
Bibliographic record
Abstract
The link between excessive consumption of dietary saturated fats and coronary heart disease (CHD) is now well established. Because of its high content of saturated fatty acids, the consumption of foods containing coconut oil may therefore be a risk factor for CHD. While the fatty acid composition of coconut oil is well established, relatively little is known about the other constituents of coconut: the milk, water, cream and meat fractions. In this study, we show that while the water fraction is low in lipid content, the milk contains about 24% of the fat content of oil and the cream and meat fractions about 34%. The other coconut constituents contain significant amounts of medium-chain triglycerides that are formed from fatty acids of chain length 8:0 to 14:0. It is these fatty acids, primarily 14:0, that are thought to be atherogenic. On the other hand, medium-chain triglycerides may be advantageous under some circumstances in that they are absorbed intact and do not undergo degradation and re-esterification processes. As a result, medium-chain triglycerides provide a ready source of energy and may be useful in baby foods or in diet therapy. Nevertheless, the possible negative effects of the saturated fatty acids and the absence of the essential fatty acid linolenic acid from all coconut constituents suggest that the coconut milk, oil and cream should not be used on a regular basis in adults.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".