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Record W2517577384 · doi:10.2983/035.035.0217

Sterol Composition of Blue Mussels Fed Algae and Effluent Diets from Finfish Culture

2016· article· en· W2517577384 on OpenAlexaff
Iyad Hailat, Christopher C. Parrish, Robert Helleur

Bibliographic record

VenueJournal of Shellfish Research · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsBiologyAlgaeMusselEffluentAquacultureFisheryShellfishBivalviaAnimal scienceFood scienceAquatic animalBotanyMolluscaFish <Actinopterygii>EcologyEnvironmental engineering

Abstract

fetched live from OpenAlex

Mussels are an excellent source of phytosterols, which have many health benefits including reduction of the level of cholesterol in the blood. This study examined free sterols in cultivated blue mussels obtained commercially and from laboratory feeding experiments. Mussels were fed algae or the effluent from cultured finfish for 6 mo, and others remained unfed for 10 wk. Mussels feeding on effluent is of interest for integrated multitrophic aquaculture. Fish waste-fed mussels had significantly higher cholesterol concentrations (710 mg/kg) compared with algae-fed mussels (409 mg/kg) and locally cultivated ones (321 mg/kg). Algae-fed mussels had significantly higher campesterol compared with locally cultivated and fish waste-fed mussels. In algae-fed mussels, 24-nordeydrocholesterol decreased significantly, and in fish waste-fed mussels, the concentration of 24-methylenecholesterol decreased significantly. In fish waste-fed mussels, the cholesterol proportion was 50.9% of total sterols and other beneficial sterols were 49.1%, whereas in algae-fed mussels, cholesterol was down to 36.7% and other sterols were 63.3%. In summary, fish waste-fed mussels had significantly higher cholesterol concentrations and proportions compared with locally cultivated and algaefed mussels, and algae-fed mussels had higher proportions of beneficial phytosterols than fish waste-fed mussels.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.685
Threshold uncertainty score0.375

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.049
GPT teacher head0.304
Teacher spread0.255 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations5
Published2016
Admission routes1
Has abstractyes

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