Bibliographic record
Abstract
Commercial interest on the application of starch nanoparticles (SNP) in food is rapidly growing. Since gums are commonly used in food processing for their texturizing functionality, it is important to understand the influence of SNP on gum rheology. λ-Carrageenan and xanthan gum were selected in this study due to their wide range of applications in food systems. SNP isolated from waxy (0% amylose) and high amylose maize (71% amylose) were used. Binary blends consisting of 0.5% (w/v) gum and different concentrations of SNP were prepared and their flow behaviors evaluated using a rheometer. The starch source, concentration and thermal stability of the SNP influenced the viscosity and viscoelastic properties of the gum-SNP blends, in particular in the low elasticity λ-carrageenan. The blends containing high concentrations of high amylose maize SNP, when compared to waxy SNP, showed higher viscoelastic stability at temperatures up to 90°C, possibly due to their higher thermal stability.
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 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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".