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Record W1990802915 · doi:10.1002/etc.2136

Environmental concentrations and bioaccumulations of cadmium and zinc in coastal watersheds along the Chinese Northern Bohai and Yellow Seas

2013· article· en· W1990802915 on OpenAlexaff
Wei Luo, Yonglong Lü, Tieyu Wang, Peiru Kong, Wentao Jiao, Wenyou Hu, Junmei Jia, Jonathan E. Naile, Jong Seong Khim, John P. Giesy

Bibliographic record

VenueEnvironmental Toxicology and Chemistry · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsUniversity of Saskatchewan
FundersState Key Laboratory of Urban and Regional EcologyChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsBioaccumulationBiotaSedimentCadmiumEnvironmental chemistryEnvironmental scienceCarpEstuaryEcologyFisheryGeologyBiologyChemistryFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Cadmium (Cd) and zinc (Zn) in surface water, sediment, carp, and crab samples collected from upstream and downstream regions of coastal watersheds along the Chinese Northern Bohai and Yellow Seas were analyzed to provide a comprehensive understanding and assessment of their environmental concentrations and bioaccumulations. The results showed that downstream waters contaminated with Zn would have adverse effects on aquatic organisms. Although nearly all sediments were contaminated with Cd due to human activities, little potential existed for Cd toxicity in sediment-dwelling fauna. Concentrations of Cd and Zn in most water, sediment, carp, and crab were less than published values. The downstream carp and crabs had higher mean bioaccumulation factors and biota-sediment accumulation factors for Cd but lower mean biota-sediment accumulation factors for Zn than the upstream carp and crabs. Based on the relationships among Cd and Zn concentrations in water, sediment, and biota, the authors conclude that Cd and Zn in crabs primarily derived from sediment exposure. Although Cd and Zn in water and sediment originated from some of the same sources, the sources of Cd or Zn in water were likely different from those in sediment.

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 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 score0.298
Threshold uncertainty score0.999

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 studies0.0000.003
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 head0.005
GPT teacher head0.203
Teacher spread0.199 · 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

Citations26
Published2013
Admission routes1
Has abstractyes

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