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Record W4389775119 · doi:10.1155/2023/5542117

A Comparative Analysis of the Nutritional Quality of Salmon Species in Canada among Different Production Methods and Regions

2023· article· en· W4389775119 on OpenAlexafffundabout
Caroline R. Gillies, Euichan Jung, Stefanie M. Colombo

Bibliographic record

VenueAquaculture Research · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyAquacultureEicosapentaenoic acidDocosahexaenoic acidFisheryChinook windNutrientFatty acidPolyunsaturated fatty acidFood scienceFish <Actinopterygii>OncorhynchusEcologyBiochemistry

Abstract

fetched live from OpenAlex

Nutritional information of fresh seafood, including salmon, is not commonly available to the public, which can lead to misconceptions. The aim of this study was to determine the nutritional content of salmon fillets, comparing: (1) Canadian salmon, both wild (pink, chinook, and sockeye) and farmed (Atlantic salmon); (2) Canadian farmed Atlantic salmon grown in ocean net pens or land-based recirculating aquaculture systems (RAS); and (3) farmed Atlantic salmon raised in Canada compared with Scotland, Chile, and Ireland. Samples were purchased from retail stores in Canada and analyzed for moisture, crude protein, total lipid, fatty acids, amino acids, cholesterol, mercury, and color. The greatest differences in nutritional content were between species, rather than if it was wild or farmed. Compared to salmon raised in net pens, salmon raised in RAS had three times more eicosapentaenoic acid (EPA) + docosahexaenoic acid (DHA) per serving (0.7/100 g vs. 2.3/100 g, respectively), twice as much omega-3s (14% vs. 30%) and redder in color (24.7 vs. 30.1) but higher in saturated fats (18% vs. 24%). Scottish salmon had over double the amount of EPA + DHA per 100 g (1.6 g) than salmon from Canada (0.70 g), Chile (0.66 g), and Ireland (0.61 g). While nutritional content differed among salmon types, each type can provide dietary essential nutrients that can benefit consumers.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.627
Threshold uncertainty score0.692

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
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 head0.192
GPT teacher head0.416
Teacher spread0.224 · 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 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

Citations7
Published2023
Admission routes3
Has abstractyes

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