Temporal ranking for characterization and improved discrimination of protein beverages
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract We propose a new temporal sensory method called temporal ranking (TR) in which assessors indicate and rank the three most noticeable sensations at every time point. The TR method was compared to temporal‐check‐all‐that‐apply (TCATA) in two trained‐panel studies, one study involving six ready‐to‐mix (RTM) protein beverages and one study involving seven ready‐to‐drink (RTD) protein beverages. In each study, the same attributes were used in both methods; six attributes were evaluated for RTMs and 10 attributes for RTDs. A trained sensory panel ( n = 10) completed TCATA and temporal ranking (TR) training exercises, then evaluated each beverage in triplicate using each method in a replicated balanced randomized design. To evaluate each temporal method (TR and TCATA), each test beverage was compared with the sucrose‐ or sucralose‐sweetened control beverage within each study (RTM and RTD). Although results from TR and TCATA often coincided, TR better differentiated the protein beverage formulations on more sensory attributes and detected differences between the test and control beverages ( p < .05) when TCATA did not. Overall, TR was found to be more sensitive in detecting sensory differences than TCATA, and thus could improve the guidance for the development and formulation of foods. Practical applications This study proposes a new temporal method, temporal ranking, which has assessors continuously rank the three most noticeable attributes when evaluating a beverage. Temporal ranking data can give improved guidance, especially for products that might have side flavors, such as natural nonnutritive sweeteners or alternative protein sources. Further application of findings and methodologies from this study may help guide development and formulation of foods.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it