Bibliographic record
Abstract
The obesity epidemic appeared in the USA in 1976-1980 and then spread across Westernized countries. This paper examines the most likely causes of the epidemic in the USA. An explanation must be consistent with the emergence of the epidemic in both genders and in all age groups and ethnicities at about the same time, and with a steady rise in the prevalence of obesity until at least 2016. The cause is closely related to changes in the American diet. There is little association with changes in the intake of fat and carbohydrate. This paper presents the opinion that the factor most closely linked to the epidemic is ultra-processed foods (UPFs) (i.e., foods with a high content of calories, salt, sugar, and fat but with very little whole foods). Of particular importance is sugar intake, especially sugar-sweetened beverages (SSBs). There is strong evidence that consumption of SSBs leads to higher energy intake and more weight gain. A similar pattern is also seen with other UPFs. Factors that probably contributed to the increased intake of UPFs include their relatively low price and the increased popularity of fast-food restaurants. Other related topics discussed include: (1) the possible importance of Farm Bills implemented by the US Department of Agriculture; (2) areas where further research is needed; (3) health hazards linked to UPFs; and (4) the need for public health measures to reduce intake of UPFs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".