Bibliographic record
Abstract
Compared to the siloed commodity departments of the past, the multi-disciplinary field of food science and technology has increasingly adopted a less segregated and more synergistic approach to research. At their most fundamental levels, all foodrelated processes from harvest to digestion are ways of bringing about, or preventing, biochemical changes. We contend that there is not a single scientific investigation of a food-related process that can avoid biochemical considerations. Even food scientists studying inorganic materials used in processing equipment and/or packaging must eventually consider potential reactions with biomolecules encountered in food systems. Moreover, since the food that we eat plays a central role in our overall well-being, it follows that tomorrow's food scientists and technologists must have a solid foundation in food biochemistry if they are to be innovators and visionaries. Introductions to biochemical topics are provided in this chapter, under the categories of carbohydrates, proteins, lipids, DNA, and toxicants. Within these broad divisions, general and specific food biochemical concepts are introduced, many of which are explored in detail in the chapters that follow.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.066 | 0.071 |
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".