Bibliographic record
Abstract
Molecular simulation continues to make an important contribution to the study of complex food systems, complementing experimental efforts to understand and manipulate food structure. The past few years have seen an increase in the complexity of systems studied, with large multicomponent systems routinely studied. For protein systems there have been advances in the simulation of protein-based Pickering emulsifier systems, such as zein particles, both as emulsifiers and as carriers of food additives such as phenolic antioxidants. Similarly, studies have moved beyond investigating just the effects of thermal processing on protein structure to include novel processing techniques involving electrical fields and high pressure and to find out how they can induce protein denaturation. The advances in the simulation of triglycerides have been particularly notable. The widespread application of coarse-graining of triglyceride structures enabled a detailed study of triglyceride crystallization and melting not yet obtained with conventional all-atom molecular dynamics. Recent polysaccharide simulations have focused on understanding the solution structure of the molecules. Simulation of inclusion complexes between starch and fatty acids that alter starch functionality and limit digestibility has revealed the importance of the starch helical secondary structure in this process. Finally, the mechanisms of the sol–helix transition during gelation of carrageenans and the adsorption of pectin structural domains at oil–water interfaces are discussed.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".