Microstructure and fractal analysis of fat crystal networks
Bibliographic record
Abstract
Abstract This paper reviews the study of the morphology and physical properties of fat crystal networks. Various microscopical and rheological methods can be used to quantify the microstructure of fats, with the ultimate aim of relating structure to mechanical response. Even though a variety of physical models have been proposed to explain the relationship between the mechanical properties of fats and their microstructure, the fractal scaling model most closely describes the experimentally observed behavior. Mass fractal dimensions determined by microscopy and rheology can be used successfully to quantify the microstructure of fats since fractal dimension values are sensitive to the combined effects of crystal size, morphology, and the spatial distribution of mass within the fat crystal network. Methods used to determine the fractal dimension of a fat crystal network such as box counting, particle counting. Fourier transform, light scattering and oil migration are explained in detail here. The relationship between fractal dimensions determined by microscopy and rheology are discussed in light of the fact that different measures of the fractal dimension describe different microstructural features in a fat crystal network.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".