Regulatory Aspects of Antioxidants
Bibliographic record
Abstract
To stop oxidative damage in the human body, antioxidants are frequently utilized in both food and pharmaceutical products. Oxidative damage is one of the root causes of many diseases, such as cardiovascular, Alzheimer's, cancer, etc. Antioxidants such as ascorbic acid, butylated hydroxytoluene, propyl gallate, butylated hydroxy anisole, and sodium metabisulphite are common excipients that are used in the finished product to reduce the oxidation of active substances. They could come from either natural or synthetic sources. Protecting food goods from oxidation-related deterioration, such as rancid fat, color changes, and nutritional loss, extends their shelf life. The approval and marketing of antioxidants based on the benefit-to-risk ratio are heavily influenced by regulatory bodies such as the European Economic Commission (EEC) in Europe, the Food and Drug Administration (FDA) in the United States, and the Food and Drug Regulations in Canada. The safety, quality, and efficacy information is essential to submit in a common technical document at the time of excipients approval. The amount of antioxidants in the food products is indicated by a nutrient content claims on the label. Moreover, inadequate doses and durations of therapy in clinical trials could explain the lack of positive outcomes.
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 | 0.020 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.013 | 0.007 |
| Insufficient payload (model declined to judge) | 0.021 | 0.011 |
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".