Bibliographic record
Abstract
Abstract Older adults represent a large and growing portion of the global population. Given the high risk of malnutrition in this population, it is important to understand factors influencing food intake; sensory perception is one of these factors. Aging is associated with a number of physiological changes that alter the way food sensory properties are perceived. Because of these changes, it is often assumed that older adults experience a decrease in food liking. Although there is little evidence to support this assumption, many studies have evaluated flavor enhancement strategies aiming to increase food liking in older adults. As older adults exhibit a high degree of heterogeneity in their liking response, more tailored approaches may be required to increase food liking in older adult populations. Current attempts to classify subgroups of older adults have had little success. Furthermore, consideration should be given to food texture in future studies, as it plays a dominant and increasingly vital role in food perception by older adults. Practical Applications Sensory perception plays an important role in food choice and intake, thus it is important to understand how foods are perceived by older adults. This is essential to the health and well‐being of older adults, as the reduction in food intake often observed with ageing is a key contributor to malnutrition in this population. The success of studies aiming to improve food liking in older adults have had very limited success to date, with some strategies actually leadings to a reduction in food consumption. To improve the success of future strategies, food perception by older adults, and the increasingly vital role of food texture must be understood.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".