Genetics of sweet taste preferences
Bibliographic record
Abstract
Sweet taste is a powerful factor influencing food acceptance. There is considerable variation in sweet taste perception and preferences within and among species. Although learning and homeostatic mechanisms contribute to this variation in sweet taste, much of it is genetically determined. Recent studies have shown that variation in the T1R genes contributes to within- and between-species differences in sweet taste. In addition, our ongoing studies using the mouse model demonstrate that a significant portion of variation in sweetener preferences depends on genes that are not involved in peripheral taste processing. These genes are likely involved in central mechanisms of sweet taste processing, reward and/or motivation. Genetic variation in sweet taste not only influences food choice and intake, but is also associated with proclivity to drink alcohol. Both peripheral and central mechanisms of sweet taste underlie correlation between sweet-liking and alcohol consumption in animal models and humans. All these data illustrate complex genetics of sweet taste preferences and its impact on human nutrition and health. Identification of genes responsible for within- and between-species variation in sweet taste can provide tools to better control food acceptance in humans and other animals.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".