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Record W6991038631

An Examination of Fish Consumption and Human Health and the Potential Role of Fish Species Diversity on Nutrient and Fatty Acid Composition

2022· dissertation· en· W6991038631 on OpenAlexaboutno aff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2022
Typedissertation
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsnot available
Fundersnot available
KeywordsNutrientPopulationPolyunsaturated fatty acidHuman healthFish farmingEssential nutrientFish <Actinopterygii>Coastal fish
DOInot available

Abstract

fetched live from OpenAlex

Fish is a major food source for the global human population and is a source of various nutrients, including unsaturated fatty acids especially n-3 polyunsaturated fatty acids (PUFA), high-quality proteins, vitamins, and minerals. Numerous studies have demonstrated that fish consumption reduces the risk of chronic disease and promotes human health. However, heavy metal and environmental contamination is a concern. The objectives of this thesis were to 1) collect evidence from the literature to determine issues related to human health and fish intake; 2) use online databases for estimates of global and Canadian fish consumption, the diversity of commercial fish species, and nutrient composition coverage of fish commonly consumed in Canada; and 3) examine the fatty acid composition of wild caught fish species consumed by an Indigenous population in northern Canada in comparison to national food nutrient databases. In the first study, a scoping review of the effects of fish consumption on human health was conducted. N-3 PUFA is the most studied nutrient component of fish in present review literatures contributing to the health benefits from fish consumption, and other nutrients in fish are understudied. Also, the potential health benefits of fish intake appear to outweigh the risk of detrimental contaminants (e.g., MeHg) in fish. The second study used FAOSTAT to determine the amount of fish produced and consumed by world and G20 and FishBase was used to determine the diversity of commercial fish species, and the Canadian Nutrient Profile (CNF), United States Department of Agriculture (USDA) and FishBase nutrient data was compared for the most popular fish consumed in Canada. G20 members consumed slightly more fish than they produced, and their fish production was dependent mainly on capture fisheries, except for China that has developed a large aquaculture industry. Low fish consumption, regions with cold water temperature and industrialization of the fish food supply appear to have less commercial fish diversity. The FishBase Nutrient Analysis Tool covered a wide range of fish species but at this time appears to be too crude to be used to estimate dietary intakes. The CNF and USDA databases did not cover all the commercial fish species for Canada as indicated by FishBase. In the third study, the fatty acid composition of Canadian Subarctic and Arctic wild freshwater fish was compared with CNF estimates except for Longnose sucker that was not available. The wild fish contained more than 135 mg EPA+DHA /100 g fish muscle but there were large differences in some estimates particularly the Cisco species. More research is required to determine the effect of other components of fish on human health rather than indiscriminately promoting fish intake. Fish diversity and variation in nutrient content due to ecological variation and errors in databases are problematic and limit the ability to assess n-3 PUFA intakes from fish consumption especially in smaller populations such as Indigenous peoples.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.313
Threshold uncertainty score0.622

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.007
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.228
Teacher spread0.215 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2022
Admission routes1
Has abstractyes

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