Bibliographic record
Abstract
Lipids not only serve as a source of condensed energy, they also provide essential fatty acids as well as mouthfeel and other attributes to food. Flavor effect of lipids is related to the interaction of food components with one another as well as lipid breakdown products via their participation in Maillard reaction under high temperature conditions experienced during processing. These reactions occur during frying, grilling and other modes of heat processing of foods. In addition, lipids may contribute to the flavor of fresh foods via lipoxygenase-assisted oxidation. Lipoxygenases are present in plants such as soybean and in fish gills, among others; their interaction with cis,cis-1,4-pentadiene moieties of lipids leads to the formation of stereospecific products. Furthermore, breakdown of lipids under thermal or photooxidative conditions produces an array of products, all of which are odor-active and may contribute to off-flavor development in both raw and processed foods. In particular, aldehydes and other carbonyl compounds serve as indicators of flavor deterioration of many foods. In addition, lipolytic reactions provide another route by which flavor reversion of food lipids may occur, such as those in butter and dairy products.
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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".