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Record W2894265262 · doi:10.5539/enrr.v8n3p182

Bioaccumulation of Trace Elements in the Organisms of Benthic Communities in the Reducing Environments of the Sea of Okhotsk

2018· article· en· W2894265262 on OpenAlexvenueno aff
С. В. Галкин, Л. Л. Демина

Bibliographic record

VenueEnvironment and Natural Resources Research · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and environmental studies
Canadian institutionsnot available
FundersRussian Science Foundation
KeywordsBioaccumulationBenthic zoneEnvironmental chemistryAbiotic componentFaunaEnvironmental scienceGillEcologyBioturbationChemistrySedimentGeologyBiologyFisheryFish <Actinopterygii>Paleontology

Abstract

fetched live from OpenAlex

A comparative study of the bioaccumulation of trace elements in benthic organisms inhabiting the area of methane seeps in the Deryugin Basin and nearby the Paramushir Island (Sea of Okhotsk) has been performed. The data pertain to both essential elements (Fe, Mn, Ni, Co, Cr, and Cu), which are necessary in certain concentrations for the metabolism of organisms, and nonessential heavy metals (Cd, Pb, and Hg), as well as As, a toxic metalloid. Dominating representatives of the bottom fauna from the Deryugin Basin (sea anemones, clams) were characterized by high bioaccumulation coefficients of most of the elements (&amp;ge; n103), regardless of their biochemical properties. The use of the coefficient K, which characterizes the accumulation of trace elements in the soft tissues of bivalves relative to their shells, allowed us to detect differences in the bioaccumulation character. Symbiotrophic bivalves of the family Vesicomyidae from the Deryugin Basin were characterized by the accumulation of all of the trace elements mostly in soft tissues (gills, primarily) compared to the carbonate shells (coefficient K &amp;gt; 1). We suppose that different effect of abiotic (biological availability of elements and water turbidity) and biotic (throphic strategy) factors of the habitats may probably be a reason of such phenomenon.

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)0.002
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 score0.011
Threshold uncertainty score0.777

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
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 head0.044
GPT teacher head0.278
Teacher spread0.234 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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