Modeling the nucleation and crystallization kinetics of a palm stearin/canola oil blend and lard in bulk and emulsified form
Bibliographic record
Abstract
Abstract Using high‐pressure homogenization to generate different droplet size distributions, the nucleation and crystallization of two fat systems [lard or a plam stearin/canola oil blend (PSCO)] were compared in bulk and emulsified form. Droplet size reduction decreased the final volume fraction of solid fat primarily in the lard system vs. the PSCO system, with a greater reduction in volume fraction using the homogenization regime that led to smaller droplets. Homogeneous and heterogeneous models showed that the nucleation rate generally decreased with a reduction in droplet size. However, the Gibbs surface energy (γ) was significantly underestimated using the homogeneous model, whereas the heterogeneous model fit the data adequately (P<0.05). The temperature sensitivity of the calculated impurity concentration in all emulsified systems was droplet size dependent. The Avrami model showed the emulsified fats to have lower Avrami indices relative to the bulk fat as well as lower crystallization rate constants. Differences in the Avrami indices and the rate constants were more pronounced in the bulk and emulsified PSCO compared with lard.
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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 | 0.000 |
| 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) | 0.000 | 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 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".