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Record W1591321826 · doi:10.1002/clen.201300668

Vertical Distribution and Mobility of Heavy Metals in Agricultural Soils along Jishui River Affected by Mining in Jiangxi Province, China

2013· article· en· W1591321826 on OpenAlexaff
Guannan Liu, Wei Xue, Tao Li, Xinhui Liu, Jing Hou, Meaghan J. Wilton, Dingshan Gao, Anjian Wang, Ruiping Li

Bibliographic record

VenueCLEAN - Soil Air Water · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSoil waterEnvironmental chemistrySmeltingHeavy metalsMetalEnvironmental scienceSoil pHOrganic matterSoil testEnvironmental engineeringSoil scienceChemistryMetallurgyMaterials science

Abstract

fetched live from OpenAlex

The vertical distribution of Cd, Cr, Cu, Pb, and Zn and their mobility (except Cr) were investigated in ten agricultural soil cores, collected near non‐ferrous metal mines and smelters along Jishui River, in Jiangxi Province, China. The surface soils near mines and smelters were contaminated by Cd, Cu, Pb, and Zn, with concentrations higher than the guideline values of China. For most polluted sites, heavy metals were mainly retained in the surface soil (0–20 cm), and the contents of them became constant in deeper soil. In all soil cores, the mean content of Cr was lower than the guideline value of China. Correlation results between studied heavy metals and soil properties showed that heavy metal contents throughout the soil profile was mostly influenced by organic matter (OM), pH, and clay content. Particularly soil OM can significantly impact on transport of soil heavy metals. There were positive correlations of OM content with all studied heavy metals, and the correlations with Cr, Cu, Pb, and Zn were significant or very significant. Heavy metal mobility in the studied region were assessed using a mobility index (MI) and mobility order was Cd ≫ Pb > Cu ≈ Zn.

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 score0.107
Threshold uncertainty score0.569

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 studies0.0000.001
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 head0.005
GPT teacher head0.194
Teacher spread0.189 · 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

Citations40
Published2013
Admission routes1
Has abstractyes

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