Bibliographic record
Abstract
[Truncated] Bread is a staple food in the human diet. It is a ‘convenient’ product delivering both satiety and nutrition, and is therefore highly desired worldwide. Traditionally, bread is made using high protein wheat as consumers generally do not like the quality of bread made with lower protein wheat. Most high protein wheat is grown in North America and exported to other countries to make bread products. However, low protein wheat is more prevalent worldwide. Attempts to improve soil conditions, plant genetics and fertilizers to bolster protein levels in non-bread making (low protein) wheat have been made. While there have been some improvements in crop health and yield, and even protein content, the improved wheat has not been comparable in bread making functionalities to those grown in North America. Looking at bread as a material, it is easy to recognize that breads have a foam-like structure. While many attempts have been made to develop the wheat varieties needed to make bread, little knowledge has been generated regarding the creation of an optimal foam structure. It is from this angle -- engineering the foam structure in bread -- that this work was carried out. This project examined the role of bubbles in bread quality, how dough is transformed into bread, the relevant properties for bubble growth in proofing dough, and whether or not pore structure in bread and bubble structure in dough could be generalized. The study was divided into four components: (1) establish the importance of pore structure on bread texture by examining a range of breads varying in texture and composition using x-ray microtomography and mechanical testing, (2) examine the importance of porosity on a given bread by developing digital versions of breads examined in (1), and changing porosity and pore structure without changing the properties of solid phases, (3) examine the growth of bubbles in dough made with Australian and Canadian flours using x-ray microtomography and rheological properties of dough, and (4) apply the percolation theory to determine the critical parameters required for defining bread structure.
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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".