Bibliographic record
Abstract
Food structure is based on a system of molecules that provide form and function. A better understanding of the arrangement of the underlying molecules that contribute to the macromolecular structure of natural foods leads to the ability to create novel food products. Microscopes give us the opportunity to image the basic architectural molecules and are tools that contribute to research into food structure. Imaging of food at a nanostructure level is possible by the use of the transmission electron microscope, the scanning electron microscope, and the atomic force microscope. The ability of these instruments to provide nanostructure resolution is based on design principles, and the quality of the preparation of the sample. The critical objective of sample preparation is to maintain the original properties of the biological material, whether it is native tissue or a complex emulsion. Only when the sample is prepared without distortion can the interpretation be considered valid. The researcher must understand the opportunities and limitations that come with each imaging method. Cryo-preparation methods are preferred for food imaging applications because freezing preserves water, fat, and air and the distribution of these constituents, which are major components in most food systems. Imaging methods must be complemented with other scientific methods to verify interpretation and eliminate bias. Correlated microscopy techniques are recommended.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.024 |
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".