Thraustochytrids and Algae as Sustainable Sources of Long-Chain Omega-3 Fatty Acids for Aquafeeds
Bibliographic record
Abstract
There is limited ability to biosynthesize long-chain omega-3 fatty acids such as EPA and DHA in food webs leading to humans. Seafood is the key source of ω3 LC-PUFA, with aquaculture expected to meet rising global demand; however, marine fish have a high dietary requirement for EPA and DHA themselves. This was traditionally met using unsustainable dietary fish oil and fish meal, but limited supply and environmental concerns have dictated research on replacements. Among the industrial sources of EPA and DHA, microalgae and especially thraustochytrids stand out as resources with high concentrations. Although unicellular, thraustochytrids are not microalgae as they are not photosynthetic but instead are microheterotrophs. This removes the light requirement and facilitates high yields of monoculture for the production of single-cell oils. The availability, in high concentrations, of usually one or the other essential fatty acid permits a calibration of the EPA and DHA dose, which is especially useful as their effects have mainly been considered together in medical and aquaculture research. EPA and DHA have different effects on cell function and are precursors of different bioactive compounds. Using thraustochytrids, microalgae, and heterotrophic dinoflagellates, the importance of DHA has been investigated. DHA was essential for optimizing the growth of the early life stages of scallops and finfish and was preferentially incorporated into fish membrane phospholipids. The production of microalgae and microheterotrophs can contribute to the treatment of wastewater and waste gas, further enhancing their sustainability and reducing the environmental costs of aquaculture.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".