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Record W2165886776 · doi:10.1002/jsfa.4241

Influence of mercury bioaccessibility on exposure assessment associated with consumption of cooked predatory fish in Spain

2011· article· en· W2165886776 on OpenAlexfundno aff
Silvia Torres-Escribano, Antonio José Ortiz Ruiz, Laura Barrios, Dinoraz Vélez, R. Montoro

Bibliographic record

VenueJournal of the Science of Food and Agriculture · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsnot available
FundersEuropean Social FundNational Research Council Canada
KeywordsMercury (programming language)Fish consumptionFish <Actinopterygii>Predatory fishDried fishFisheryMERCURY EXPOSUREFood scienceFish productsToxicologyBiologyEnvironmental chemistryEnvironmental scienceChemistryBiomonitoring

Abstract

fetched live from OpenAlex

BACKGROUND: Predatory fish tend to accumulate high levels of mercury (Hg). Food safety assessment of these fish has been carried out on the raw product. However, the evaluation of the risk from Hg concentrations in raw fish might be modified if cooking and bioaccessibility (the contaminant fraction that solubilises from its matrix during gastrointestinal digestion and becomes available for intestinal absorption) were taken into account. Data on Hg bioaccessibility in raw predatory fish sold in Spain are scarce and no research on Hg bioaccessibility in cooked fish is available. The aim of the present study was to evaluate Hg bioaccessibility in various kinds of cooked predatory fish sold in Spain to estimate their health risk. RESULTS: Both Hg and bioaccessible Hg concentrations were analysed in raw and cooked fish (swordfish, tope shark, bonito and tuna). There were no changes in Hg concentrations during cooking. However, Hg bioaccessibility decreased significantly after cooking (42 ± 26% in raw fish and 26 ± 16% in cooked fish), thus reducing in swordfish and tope shark the Hg concentration to which the human organism would be exposed. CONCLUSION: In future, cooking and bioaccessibility should be considered in risk assessment of Hg concentrations in predatory 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.277
Threshold uncertainty score0.305

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 studies0.0000.001
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 head0.021
GPT teacher head0.247
Teacher spread0.227 · 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

Citations46
Published2011
Admission routes1
Has abstractyes

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