Bibliographic record
Abstract
A growing proactive health movement is driving consumers to treat food as a means to prevent, manage and possibly even reverse certain conditions, Food Business News reported Aug. 27. “What we see is an increase in the number of people who are trying products to make themselves better in the long term,” said Darren Seifer, a food and beverage industry analyst for The NPD Group, a market research company. Around 80% of consumers have adopted a “food as medicine” approach to eating, according to Nielsen, the data analytics company, and The NPD Group found a quarter of U.S. adults are actively trying to manage their health through food. Dairy, sodium and sugar are the most commonly avoided foods. While older adults may seek medicinal foods to treat physical ailments like aging joints or improve heart health, younger people are turning to food to manage issues like stress and anxiety. “Gen Z is starting to expect mental health as part of an overall health and wellness regimen,” Seifer said. “It's not just about parts of the body you can see or feel, it's also about the mind.”
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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.244 | 0.120 |
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".