Dataset on the small- and large deformation mechanical properties of emulsion-filled gelatin hydrogels as a model particle-filled composite food gel
Bibliographic record
Abstract
In this article we present data related to the original research articles ‘Effect of matrix architecture on the elastic behavior of an emulsion-filled polymer gel’ (Gravelle et al., 2021) and ‘The influence of network architecture on the large deformation and fracture behavior of emulsion-filled gelatin gels’ (Gravelle and Marangoni, 2021). The small deformation elastic (Young's) modulus and large deformation fracture behavior of emulsion-filled composite gelatin gels are reported as a function of filler volume fraction (ϕf = 0 – 0.32). Homogeneous and heterogeneous network architectures were achieved by varying electrostatic interactions between matrix and filler. The effect of emulsion droplet physical state (solid fat or liquid oil) and gelator concentration (2, 4, 6, or 8% gelatin) were also evaluated. The reported elastic modulus, and fracture properties were obtained from large deformation uniaxial compression tests. Power law scaling behavior was identified for the elastic modulus as a function of both ϕf and gelator concentration, which are also reported. This data is relevant to the evaluation of network properties on the applicability of small deformation particle reinforcement theories and models describing the fracture mechanics of filled composites such as fat-filled food systems.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".