Bibliographic record
Abstract
Lipids are widely present in food and biological systems and their irreplaceable role in nutritional and health benefits has been well understood. Dietary lipid supplements, especially those containing functional fatty acids, such as omega-3 fatty acids, as well as other bioactive compounds, play an important role in the nutrient supplement industry. Dietary supplementation of lipids is particularly attractive to people who cannot obtain those lipids from their diet or require enhanced intake of them to maintain or to improve health. Dietary lipid supplements can be obtained from marine origin (including fish oil, seal blubber oil, krill oil, and algal oil) and terrestrial plant origin (including evening primrose oil, borage oil, black cumin seed oil, pumpkin seed oil, berry seed oils, and sea buckthorn seed oil). There has been growing research and commercial interest in dietary lipid supplements for their diverse health benefits, especially for the management and treatment of different health conditions. There has always been public concern that dietary supplements should be safe and of good quality, and thus the risks of these supplements with potential health risks should be fully evaluated, and the safety of consuming oils from marine origin as well as those from terrestrial sources should be further assessed in areas of environmental contaminants. This chapter aims to provide an account of different lipid supplement classes and their chemical compositions and health benefits.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.098 | 0.070 |
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".