You Are What You Eat: California Sea Cucumbers Become “Fishier” After Integrated Multi-Trophic Aquaculture with Chinook Salmon
Bibliographic record
Abstract
Recent interest in integrated multi-trophic aquaculture (IMTA) as an ecologically-sustainable and climate-conscious aquaculture system has resulted in testing different species partnerships and configurations in anticipation of industrialization. Deposit feeders like the California sea cucumber (Apostichopus californicus) have been suggested as ideal partners for IMTA with finfish, due to their ability to consume fish waste as well as their passive nature. However, the nutritional impacts of feeding on fish waste in IMTA have not yet been established for this species. The present study tested the effect of 3 months of inclusion in IMTA with Chinook salmon (Oncorhynchus tshawytscha) on the fatty-acid and nitrogenous-metabolite profiles of California sea cucumbers. The fatty-acid profiles of IMTA sea cucumbers showed significant changes from wild reference individuals, while few differences were detected in amino acids and other nitrogenous metabolites. Sea cucumbers housed directly in cages with salmon showed distinct shifts in their fatty-acid profiles toward higher levels of MUFAs and lower levels of SFAs, while PUFA concentrations remained the same. Sea cucumbers included in IMTA with finfish may be even more healthful for humans due to the accumulation of certain unsaturated fatty acids in their tissues not seen in wild reference individuals.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".