Bibliographic record
Abstract
Processed foods have been part of the human diet from the very earliest times. Recently, processed foods have come under scrutiny, particularly the category ultraprocessed foods as defined in the NOVA classification of foods. The basic tenet behind this renewed concern about ultraprocessed foods is that it is processing per se, which matters in diet and health, not nutrients or foods. Notwithstanding this, the literature on ultraprocessed foods is almost entirely focused around nutrients and obesity. However, not all studies have found positive links between obesity and ultraprocessed food intake. The category, ultraprocessed foods, is large, accounting for approximately 60% of energy intake and 90% of added sugar intake. The advocates of the NOVA system advise that the intakes of these foods should be avoided, but the scientific basis for this advice is very weak. Thus, a reduction in ultraprocessed foods has been advocated covering 16 foods to reduce US intakes of added sugar. However, when US food consumption data are examined on a food-by-food basis, only 6 of these 16 foods are associated with high added sugar intakes. Data from the United States, United Kingdom, Canada, and Brazil fail to show a relationship between percent energy from ultraprocessed foods and the intakes of fats, saturated fatty acids, or sodium. There is a positive association between ultraprocessed food intake and the intake of added sugar. A negative correlation with dietary fiber is found. This is not surprising, because almost all added sugar is found in the category, ultraprocessed foods, while the majority of dietary fiber is excluded. When compared with the scientific literature, there is little scientific basis for limiting the use of infant foods, fat spreads, or commercially prepared breads in the present diet.
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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.016 | 0.010 |
| Insufficient payload (model declined to judge) | 0.015 | 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".