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Record W2899867619 · doi:10.1080/23311932.2018.1546790

Whole fish vs. fish fillet—The risk implications for First Nation subsistence consumers

2018· article· en· W2899867619 on OpenAlexafffundabout
Claire McAuley, Daniel C. Smith, Ave Dersch, Bart Koppe, Stacey Mouille-Malbeuf, Darryel Sowan

Bibliographic record

VenueCogent Food & Agriculture · 2018
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsIntrinsik (Canada)
FundersHealth Canada
KeywordsPikeFish consumptionEsoxFillet (mechanics)FisheryFish filletSubsistence agricultureMercury (programming language)FishingFish productsFish <Actinopterygii>BiologyEcologyAgricultureEngineeringComputer science

Abstract

fetched live from OpenAlex

Consumption advisories associated with mercury concentrations are typically based on consumption of fish fillets; however, many First Nation community members consume more than just the fish fillet because of both preference and availability. Food frequency questionnaires were completed by 106 community members to identify which parts of the fish were typically and preferentially consumed. The results of the questionnaires showed that, depending on the species of fish, between 20% and 100% of the respondents ate more than just the fish fillet. Two northern Alberta First Nations harvested 73 piscivorous fish as part of separate studies investigating fish quality. Fillets and whole fish from two species, northern pike (Esox lucius) and walleye (also known as pickerel; Sander vitreus), were analyzed for mercury concentrations. Measured mercury concentrations in whole fish were significantly lower than in fillets (p < 0.05 in all cases). This paper investigates the implications of fish consumption advisories for First Nation communities where many subsistence consumers eat more than just the fish fillet. Consideration of traditional consumption practices may result in a more accurate assessment of exposure for the development of fish consumption guidelines.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.278
Threshold uncertainty score0.990

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 studies0.0110.000
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 head0.051
GPT teacher head0.316
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 designNot applicable
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

Citations4
Published2018
Admission routes3
Has abstractyes

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