Market validation through customer-focused sensory analysis of functional bakery goods
Bibliographic record
Abstract
With growing market demand for healthy diet, more and more people are looking for easy-to use, healthy, and tasty bakery products. The pandemic had seen an increase in home baking with people desiring to bake their own bread and cakes. Sourdough is considered to be healthier and tastier than conventional bread due to long fermentation times. This bread must be made from scratch, requiring time and resources. A company has developed a ready-to-use dry multigrain sourdough bread mix using local ingredients that takes less time to prepare while still allowing consumers to have a home baking experience. A sugar-free cake mix was also formulated which could be used to make cakes, muffins, and cup cakes. This healthy high-fibre mix is a basic mix which has endless possibilities of making variations as per customers choice such as adding raspberry, blueberry, or chocolate. Market validation is needed to better understand the potential market demand and consumer requirements for the product attributes of these two products (e.g., flavor, texture, ingredient, price etc.). For the test, the mixes were home delivered to the interested participants. They had to bake the products, taste them and complete an online survey. The survey had questions on various aspects - demographics, product baking experience, taste and texture of product, liking, recommendation, consumption and purchase intent.
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.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.021 |
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".