Handbook of Food Structure Development
Bibliographic record
Abstract
The most useful properties of food, i.e. the ones that are detected through look, touch and taste, are a manifestation of the food’s structure. Studies about how this structure develops or can be manipulated during food production and processing are a vital part of research in food science. This book provides the status of research on food structure and how it develops through the interplay between processing routes and formulation elements. It covers food structure development across a range of food settings and consider how this alters in order to design food with specific functionalities and performance. Food structure has to be considered across a range of length scales and the book includes a section focusing on analytical and theoretical approaches that can be taken to analyse/characterise food structure from the nano- to the macro-scale. The book concludes by outlining the main challenges arising within the field and the opportunities that these create in terms of establishing or growing future research activities. Edited and written by world class contributors, this book brings the literature up-to-date by detailing how the technology and applications have moved on over the past 10 years. It serves as a reference for researchers in food science and chemistry, food processing and food texture and structure.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.117 | 0.082 |
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".