An Investigation Into Soup With the Addition of Brown Seaweed (<scp><i>Ascophyllum nodosum</i></scp>) and Red Seaweed (<scp><i>Chondrus crispus</i></scp>) Using Nonconsumers of Seaweed
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 Seaweed has been proposed as an ingredient that can increase the umami taste and saltiness of food items. However, seaweed is not regularly consumed in North America. This study aimed to evaluate how nonconsumers of seaweed ( n = 103) perceive the sensory properties and acceptance of soup with brown seaweed ( Ascophyllum nodosum ) and red seaweed ( Chondrus crispus ) powder added. The samples include a control soup (without seaweed) and soup with 1.5% and 3% brown seaweed, as well as 1.5% and 3% red seaweed by weight. Furthermore, before evaluating the soup, they were asked to identify the flavors and textures they associate with seaweed. The brown and red seaweed increased the umami and saltiness intensity of the soup, but it also increased the bitterness and sourness. The red seaweed also decreased the sweetness, overall liking, and liking of the soup's flavor. The participants associated seaweed with fishy, salty, and umami flavors and undesirable textures (slimy, tough, chewy). Seaweed increased the umami and salty taste of soup when evaluated by nonconsumers, but it also introduced other tastes to the soup. This study also identified nonconsumers’ beliefs about seaweed and should help create novel food products using seaweed.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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