A Probabilistic Approach to Model the Nonisothermal Nucleation of Triacylglycerol Melts
Bibliographic record
Abstract
Crystallization studies are usually performed under isothermal conditions. Kinetic parameters characterizing the isothermal nucleation and growth processes can be obtained using classical nucleation and growth models. However, crystallization regimes found in nature, as well as those used in food and pharmaceutical processing, are rarely isothermal. Focusing on the nucleation stage, the approach followed in this work was to define a new parameter to characterize the driving force of nucleation, the supercooling-time exposure (β), which not only depends on the difference between the melting temperature ( T m ) and the onset temperature of nucleation ( T c ) Δ T c but also on the induction time of nucleation, t c, and therefore the cooling rate (φ), namely, β = (1/2)Δ T c t c = Δ T c 2 /2φ. An exponential probability density function of the values of β was utilized to model changes in nucleation rate as a function of β in the form J / J max = . From this parametrization procedure, the energy of activation for the nucleation process in palm oil, milkfat, and other palm oil based fats could be estimated.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".