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Record W2560596828 · doi:10.1021/acs.est.6b05068

China’s Soil Pollution Control: Choices and Challenges

2016· article· en· W2560596828 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEnvironmental Science & Technology · 2016
Typearticle
Languageen
FieldEngineering
TopicEnvironmental remediation with nanomaterials
Canadian institutionsUniversity of Saskatchewan
FundersCanada Research Chairs
KeywordsChinaBeijingLibrary scienceChinese academy of sciencesReuseCitationEnvironmental pollutionDanishPolitical scienceEngineeringGeographyEnvironmental protectionComputer scienceArchaeology

Abstract

fetched live from OpenAlex

Protection (MEP) and Ministry of Land and Resources (MLR) conducted a 9 year survey of contaminants in soils, which considered about two-thirds of the land across the Chinese mainland.According to the results, 19.4% of the farmland surveyed was classified as polluted 1 (Figure 1).Contamination of soil could have adverse implications for food security. 2In recent years, concern about safety of agricultural produce exploded onto the public stage, especially after the frightening media stories about concentrations of the metal, cadmium in Hunan-grown rice in 2013.Due to recent, rapid urbanization, China's demand for land to be developed has been increasing continuously.Consequently, previous industrial sites are often reclassified for urban development.The national survey indicated that onethird of sites in and around 690 highly polluting enterprises and 146 industrial parks are polluted. 1Redeveloping these contaminated sites is a concern because of chronic risks to health of residents.It is noteworthy that China's worst explosion of warehouses in Tianjin on August 12, 2015 will

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score0.509

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 head0.177
Teacher spread0.172 · 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