Bibliographic record
Abstract
Abstract The past two decades has produced an ever increasing awareness of the role of dietary fat in the etiology of different chronic diseases (e.g. diabetes, coronary heart disease and cancer). In addition, advances in food technology methods for preserving and processing food lipids for retained nutritional and sensory appeal has coincided with increased awareness concerning the safety of dietary fats and oil sources, as they relate to both the visible and non‐visible components of the total crude lipid fraction. Increased calorie consumed from fat sources not only provides consumer exposure to natural fat and oil components (e.g. fatty acids, sterols etc.), but also derived products of oxidation and hydrogenation and the presence of natural, environmental (pollutants) or intentional (e.g. additives) xenobiotics which co‐exist, or accumulate, in the crude lipid fraction. Thus, understanding the safety of dietary fats and oils requires not only an awareness of the elements of lipid chemistry of soluble constituents in the lipid phase, but also the associated reaction conditions that may convert them to toxic products. Moreover, by predicting a risk from the combined relative toxicity and the level of exposure to the organism will enable assessment of a hazard to exposure to these chemicals. In this chapter, a number of reactive and labile fat soluble constituents are assessed for safety and potential toxicity in regard to both initiating and propagating the cascade of events that may lead to a toxic end‐point measure. Endogenous (e.g. co‐oxidation reactions) as well as exposure to exogenous (e.g. photoxidation or presence of man made pollutants) xenobiotics are analyzed in respect to the potential for inducing adverse health effects.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".