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Record W4409049417 · doi:10.3390/fishes10040154

You Are What You Eat: California Sea Cucumbers Become “Fishier” After Integrated Multi-Trophic Aquaculture with Chinook Salmon

2025· article· en· W4409049417 on OpenAlexaff
Emaline M. Montgomery, B. Cannon, Ian Forster, Christopher M. Pearce

Bibliographic record

VenueFishes · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEchinoderm biology and ecology
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsChinook windFisheryAquacultureTrophic levelBiologyFish <Actinopterygii>EcologyOncorhynchus

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.009
GPT teacher head0.203
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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