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Metals in tissues of seabass and seabream reared in sites with oxic and anoxic substrata and risk assessment for consumers

2015· article· en· W1474455779 on OpenAlexfundno aff
Ioanna Kalantzi, Spiros A. Pergantis, Kenneth Black, Tracy M Shimmield, Nafsika Papageorgiou, Manolis Tsapakis, Ioannis Karakassis

Bibliographic record

VenueFood Chemistry · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsnot available
FundersNational Research Council CanadaU.S. Environmental Protection Agency
KeywordsGillHazard quotientArsenicBiologyAnimal scienceFisherySeleniumEcotoxicologyFish farmingEnvironmental chemistryAquacultureVeterinary medicineFish <Actinopterygii>ToxicologyChemistryHeavy metals

Abstract

fetched live from OpenAlex

Twenty-eight metals and elements were measured in the muscle, liver, gills, bone and intestine of farmed seabass and gilthead seabream from four Mediterranean fish farms. The influence of fish species and the effect of environmental conditions on the metal accumulation in fish tissues was investigated. Most concentrations were lower in muscle and higher in liver and bone than in other body tissues. Seabass accumulates more elements in its tissues than seabream. Fish reared in coarse, oxic sites accumulate more elements with higher concentrations in muscle, bone and intestine and with lower concentrations in liver and gills than fish reared in silty, anoxic sites. This may be attributed to feed type and sediment properties. According to the metal pollution index, hazard quotient, selenium health benefit values, carcinogenic risk of arsenic, maximum safe consumption and the permitted limits, the consumption of both farmed species should be considered as safe for human health.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.365
Threshold uncertainty score0.284

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 studies0.0000.000
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 head0.029
GPT teacher head0.283
Teacher spread0.254 · 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

Citations103
Published2015
Admission routes1
Has abstractyes

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