Determinants of Sweetness Preference: A Scoping Review of Human Studies
Bibliographic record
Abstract
Factors associated with sweetness preference are multi-faceted and incredibly complex. A scoping review was undertaken to identify determinants of sweetness preference in humans. Using an online search tool, ProQuest ™, a total of 99 publications were identified and subsequently grouped into the following categories of determinants: Age, dietary factors, reproductive hormonal factors, body weight status, heritable, weight loss, sound, personality, ethnicity and lifestyle, previous exposure, disease, and 'other' determinants. Methodologies amongst studies were heterogenous in nature (e.g., there was variability across studies in the sweetness concentrations tested, the number of different sweetness concentrations used to assess sweetness preference, and the methods utilized to measure sweetness preference), rendering interpretation of overall findings challenging; however, for certain determinants, the evidence appeared to support predictive capacity of greater sweetness preference, such as age during certain life-stages (i.e., young and old), being in a hungry versus satiated state, and heritable factors (e.g., similar sweetness preferences amongst family members). Recommendations for the design of future studies on sweetness preference determinants are provided herein, including an "investigator checklist" of criteria to consider.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".