Strain Dependence of the Uniaxial Compression Response of Vegetable Shortening
Bibliographic record
Abstract
Abstract Many soft food materials, including vegetable shortening, exhibit complex rheological behavior. For shortening, a precise determination of rheological behavior is necessary to understand its functionality as a food ingredient. Commercial vegetable shortening was subjected to monotonic and cyclic uniaxial compression tests at a wide range of loading rates. The elastic modulus determined from unloading was a function of strain, varying between 740 kPa in the shortening's strain hardening region to 220 kPa at large strain where perfect plasticity had developed. Visual analysis of shortening specimens during the compression process showed that a rate‐dependent stress overshoot was attributable to the development of a shear band following strain hardening. An elastoviscoplastic constitutive model was developed to define the complex rate‐dependent compression response of vegetable shortening. Using the fundamental parameters obtained from the different types of compression tests, the proposed model accurately predicted the uniaxial compression response of vegetable shortening over a wide range (three decades) of compression rates. A model with predictive capabilities of large strain properties is desirable because shortening is subject to large strain in essentially all applications.
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) | 0.000 | 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) | 0.001 | 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".