Bibliographic record
Abstract
Abstract Marine oils are obtained from the flesh of fatty fish, liver of lean whitefish, and blubber of marine mammals. Lipids from marine fish have been used as food and medicine, and traditional uses of blubber lipids of marine mammals were mostly industrially oriented, except for Innus and Eskimos. Marine mammal oils were used as lubricants or “train” oils as well as fuel and used for lighting. Research findings on the importance of long‐chain polyunsaturated fatty acids (LC PUFA) in human health have opened new channels for their value‐added use in food and pharmaceutical industries. During the last three decades, it has been established that Greenland Eskimos living on their traditional diet have a lower incidence of coronary heart disease than do Danes living on a western‐style diet. It has been recognized that PUFA could be useful in controlling serum triacylglycerols, but the fatty acids provided by the food industry were often of the Ω6 family. This article summarizes the current knowledge available on marine mammal oils with focus on seal and whale oils. It presents the lipid class, fatty acid compositions, and oxidative stability of marine mammal oils. The process, production of omega‐3 fatty acid concentrates, and application of common marine mammal oils are discussed. The health benefits and disease prevention properties of seal oils, especially the two unique ingredients, namely docosapentaenoic acid (DPA) and long‐chain monounsaturated fatty acids, (LC‐MUFA) are reviewed. Finally, the fatty acid profile, position distribution, and health benefits of marine mammal oils are compared with those of fish oils.
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 | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.034 | 0.024 |
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".