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Record W1927145055 · doi:10.1002/clen.201400569

Evaluation of Lead in Arable Soils, China

2015· article· en· W1927145055 on OpenAlexaff
Xiuying Zhang, Dongmei Chen, Taiyang Zhong, Xiaomin Zhang, Min Cheng, Xinhui Li

Bibliographic record

VenueCLEAN - Soil Air Water · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsQueen's University
FundersNational Natural Science Foundation of China
KeywordsArable landSoil waterEnvironmental scienceSmeltingChinaIrrigationEnvironmental protectionGeographyAgricultureAgronomySoil scienceBiologyChemistryArchaeology

Abstract

fetched live from OpenAlex

Lead (Pb) contamination in arable soils is one of the most serious ecological problems due to its high toxicity on human health. Thus, we need to understand the concentration level, contaminated area, and spatial distribution of Pb in arable soils on regional or national scale. This paper reviewed the studies on Pb concentrations throughout Chinese arable soils, based on relevant 537 studies from 2002 to 2014. The results showed that the average Pb concentration was 34.41 mg/kg, higher than its background of 23.50 mg/kg, indicating that Pb has been introduced into soil from exterior sources. Mining and smelting activities, irrigation by wastewater, and urban development greatly contributed to Pb accumulation in arable soils. North China had lower Pb concentrations than the south, and many hotspots existed on the Pb concentration map due to mining and smelting activities. On the provincial scale, arable soils in Yunnan, Guangxi, and Shaanxi Provinces were moderately polluted by Pb, Gansu and Shaanxi Provinces were slightly affected by Pb, while the other provinces showed relative safe levels.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.648
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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)0.0020.003

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 head0.046
GPT teacher head0.271
Teacher spread0.225 · 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; both teacher heads agree on what is shown here.

Study designSimulation or modeling
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

Citations14
Published2015
Admission routes1
Has abstractyes

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