A Healthier Beverage Choice is Based on a Subjective Assessment of Sweet Taste
Bibliographic record
Abstract
Despite promising interventions to lower people’s daily sugar consumption, such as health- or taste-focused labels, the consumption of sugar-sweetened beverages (SSBs) continues to rise. To improve the effectiveness of existing labels, the way people process sugar amounts in grams (g) as displayed on beverages seems to merit elucidation. For example, do people perceive the difference in the amount of sugar, and thus in the subjective sweet taste, between two beverages according to Weber’s law? Additionally, is that perceived difference the cause of their beverage choice? In order to investigate these questions, participants in this online experiment first had to estimate the sugar difference between two beverages based on grams and then decide whether they would switch to a lower-sugar beverage. We found that participants’ different estimates followed Weber’s law. The choice of the lower-sugar beverage, however, depended on how large they personally perceived that difference. In other words, the choice was independent of the ratio. These results show that future labels, rather than indicating the total amount of sugar, should indicate whether the reduction, for example in the amount of sugar compared to another beverage, was perceived as significant by others.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".