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Record W2184183029 · doi:10.2112/si65-006.1

Metal contamination and potential toxicity of sediment from lock gate port in South Korea

2013· article· en· W2184183029 on OpenAlexfundno aff
Ki Young Choi, Suk Hyun Kim, Gi Hoon Hong, Chang Joon Kim

Bibliographic record

VenueJournal of Coastal Research · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsnot available
FundersNational Research Council CanadaMinistry of Land, Transport and Maritime Affairs
KeywordsContaminationSedimentEnvironmental chemistryEnvironmental sciencePollutionChemistryGeologyEcologyBiology

Abstract

fetched live from OpenAlex

Choi, K.Y., Kim, S.H., Hong, G.H., Kim, C.J., 2013. Assessment of the metal contamination and potential toxicity of sediment from artificially closed system port in South Korea.A study was performed to determine the total and potentially bioavailable heavy metal concentrations in sediments from the Port of Incheon, and the differences in contamination for each pier were identified. Metal enrichment factors (EF) suggested that contamination with Cu, Zn, Cd, Pb, and Hg was occurring at the port. Ni, Cu and Zn concentrations exceeded the effect range low level at most sampling sites according to U.S. NOAA sediment quality guidelines (SQGs). The potential toxicity of metals was determined by 1M HCl extractions. Large portions of Cu, Zn, Cd, and Pb were present as potentially bioavailable fractions (1M HCl extractable fractions) and they were introduced from anthropogenic activities. Principal component analysis (PCA) was performed in order to assess the sources of contamination of the sediment and the influence of anthropogenic activities on sediment quality. Two PCA factors were obtained for identifying the sampling sites affected by anthropogenic activities. Patterns of sediment contamination at each pier were classified, and the results showed that Cu–Zn–Cd–Pb–Hg and Ni were the main components.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.749
Threshold uncertainty score0.999

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 studies0.0000.000
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 head0.029
GPT teacher head0.301
Teacher spread0.272 · 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

Citations6
Published2013
Admission routes1
Has abstractyes

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