MétaCan
Menu
Back to cohort

Fish Intake and Serum Fatty Acid Profiles From Freshwater Fish

2007· article· en· W2434441756 on OpenAlexaff
A. Philibert

Bibliographic record

VenueEpidemiology · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsFish productsFish <Actinopterygii>Fatty acidDocosahexaenoic acidFreshwater fishFish consumptionAnimal scienceBlood lipidsBiologyFood scienceChemistryCholesterolPolyunsaturated fatty acidEndocrinologyFisheryBiochemistry

Abstract

fetched live from OpenAlex

ISEE-195 Objective: This study examined the FA pathway from fish to serum among 243 moderate, lake-side fish-eaters. Material and Methods: A food-frequency questionnaire was used to determine caught and market-bought fish species intake (mean: 58 g/day + 63). Freshwater catch averaged 45% of total fish intake. Fish were categorized into “lean” and “fatty” on the basis of their EPA + DHA content, estimated from published data. Serum FA concentrations were determined by gas chromatography. Results: Results showed no relation between total fish intake or estimated n-3 FA intake from all fish and serum n-3 FA concentrations. Only fatty fish intake, particularly salmonid, and estimated EPA + DHA intake from fatty fish were significantly associated with serum EPA + DHA (R2 = 0.41 and 0.40, respectively). No relation was observed between local catch or estimated FA intake from local catch and serum n-3 FA. Age, sex, and lipid metabolism medication were associated with serum n-3 FA concentrations. Neither blood selenium nor blood Hg was associated with serum FA. Conclusions: We conclude that the relation between fatty fish consumption and serum n-3 FA cannot be generalized to all fish intake.

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 score0.118
Threshold uncertainty score0.999

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)0.0020.000

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 head0.041
GPT teacher head0.267
Teacher spread0.226 · 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

Citations2
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueEpidemiologySame topicAquaculture Nutrition and GrowthFrench-language works237,207