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Record W2041195275 · doi:10.1080/10807039.2012.759474

Omega-3 Fatty Acids in Fish from the Laurentian Great Lakes Tribal Fisheries

2012· article· en· W2041195275 on OpenAlexaff
Michael D. Moths, John A. Dellinger, Bruce J. Holub, Michael Ripley, Joseph McGraw, Ronald E. Kinnunen

Bibliographic record

VenueHuman and Ecological Risk Assessment An International Journal · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of Guelph
FundersUniversity of Wisconsin-Milwaukee
KeywordsSalvelinusFisheryTroutBioaccumulationFreshwater fishEcologyLatitudeBiologyFish <Actinopterygii>Environmental scienceGeography

Abstract

fetched live from OpenAlex

ABSTRACT Dietary fish must be assessed for benefits and risks to formulate risk management strategies. This article demonstrates that Laurentian Great Lakes (GL) freshwater species are good sources of omega-3 fatty acids using new data from a small sample (n = 7) of Lake Superior siscowet lake trout (Salvelinus namaycush siscowet) and five other GL fish species’ data. For Lake Superior (LS) siscowets, the saturates, mono-unsaturates, and poly-unsaturates composed 20.1, 40.7, and 39.1% of total lipid weight, respectively. Omega-3 poly-unsaturates (PUFAs) in these fish were more than twice the omega-6 (omega 3/6 ratio = 2.4). The LS lake trout data were combined with earlier LS data collected during the 1980s for eight other species and from five species of Lake Erie fish. All the GL freshwater species were compared with seven other published marine and freshwater fish studies from other global regions. PUFAs were compared based on latitude and marine versus freshwater origin. Differences between marine and freshwater species in omega-3 fatty acid were less at higher latitudes. GL freshwater fish species can be a good source of beneficial fats like marine fish and must be accounted in effective risk communications involving persistent bioaccumulative toxicants in dietary fish.

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 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 score0.028
Threshold uncertainty score0.989

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.000

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 head0.026
GPT teacher head0.291
Teacher spread0.265 · 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

Citations18
Published2012
Admission routes1
Has abstractyes

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