Food Color, Taste, Smell, Culinary Plate, Flavor, Locale, and their Impact on Nutrition: Present and Future Multisensory Food Augmentation and Noncommunicable Disease Prevention: An Overview
Bibliographic record
Abstract
Cognizant that 'the world is one family', this overview describes chemosensory characteristics of food and related issues that may enable global inequalities in healthy food consumption to be improved with a reduction in noncommunicable diseases (NCDs), preventatively. Past and modern aspects of food tradition are briefly described followed by titular chemosensory characteristics and their potential application to improving health in nutrition in the sense intended, including the culinary plate. Human-computer interface and food augmentation reality and commensal dining, in association with chemosensory properties, including sound concerning oral food processing, are described. Future research on arresting trends in the prevalence of NCD is suggested based on the literature. Visual cues for in-store food choice are discussed that potentially allow the consumer, through psychological processes and behavior outcomes, to be more discerning. Advertisements and store architecture per se are not discussed. The relatively high prevalence of anosmia caused by COVID-19 infection relative to non-infected subjects may alter taste and flavor perception and lead to changed dietary habits and metabolism. Most global consumers can practice the 'how' and ‘when’ to beneficially eat but food insecurity poses a global problem.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.011 |
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".