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Record W2518209804 · doi:10.3390/su8080795

Spatial Distribution of Mercury (Hg) Concentration in Agricultural Soil and Its Risk Assessment on Food Safety in China

2016· article· en· W2518209804 on OpenAlexaff
Shanqian Wang, Taiyang Zhong, Dongmei Chen, Xiuying Zhang

Bibliographic record

VenueSustainability · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsQueen's University
FundersNational Natural Science Foundation of China
KeywordsEnvironmental scienceMercury (programming language)PollutionAgricultureSpatial distributionChinaAgricultural landOrchardEnvironmental protectionEnvironmental engineeringEnvironmental chemistryGeographyAgronomyChemistryEcology

Abstract

fetched live from OpenAlex

Soil mercury (Hg) pollution in some areas of China is a serious problem and has aroused a lot of attention on a local scale. However, there are few studies on Hg pollution on a national scale. This study collected 444 published papers during 2005–2015 on Hg concentrations in agricultural soil throughout China, under seven land uses, namely: dry land, paddy field, vegetable field, tea garden, orchard, traditional Chinese medicine field and tobacco field, to assess the spatial distribution of Hg concentration and evaluate its influence on food safety. The averaged Hg concentration (0.108 mg/kg) was higher than its background (0.065 mg/kg), but much lower than the guidelines (GB15618-1995 II) for crop production. The spatial distribution of Hg throughout China showed great variability, with some hotspots due to Hg related mining and smelting activities. According to the Environment Quality Standard for soil in China (GB15618-1995 II), 4.2% of agricultural soil should be abandoned due to Hg pollution, and 2.0% faced a high risk of Hg pollution.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.015
Threshold uncertainty score0.324

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.006
GPT teacher head0.251
Teacher spread0.245 · 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

Citations35
Published2016
Admission routes1
Has abstractyes

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