Evolution of the Human Diet: Linking Our Ancestral Diet to Modern Functional Foods as a Means of Chronic Disease Prevention
Bibliographic record
Abstract
The evolution of the human diet over the past 10,000 years from a Paleolithic diet to our current modern pattern of intake has resulted in profound changes in feeding behavior. Shifts have occurred from diets high in fruits, vegetables, lean meats, and seafood to processed foods high in sodium and hydrogenated fats and low in fiber. These dietary changes have adversely affected dietary parameters known to be related to health, resulting in an increase in obesity and chronic disease, including cardiovascular disease (CVD), diabetes, and cancer. Some intervention trials using Paleolithic dietary patterns have shown promising results with favorable changes in CVD and diabetes risk factors. However, such benefits may be offset by disadvantages of the Paleolithic diet, which is low in vitamin D and calcium and high in fish potentially containing environmental toxins. More advantageous would be promotion of foods and food ingredients from our ancestral era that have been shown to possess health benefits in the form of functional foods. Many studies have investigated the health benefits of various functional food ingredients, including omega-3 fatty acids, polyphenols, fiber, and plant sterols. These bioactive compounds may help to prevent and reduce incidence of chronic diseases, which in turn could lead to health cost savings ranging from $2 to $3 billion per year as estimated by case studies using omega-3 and plant sterols as examples. Thus, public health benefits should result from promotion of the positive components of Paleolithic diets as functional foods.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".