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Record W1985451800 · doi:10.4491/eer.2013.18.4.241

Deriving Ecological Protective Concentration of Cadmium for Korean Soil Environment

2013· article· en· W1985451800 on OpenAlexaboutno aff
Woo-Mi Lee, Sun‐Hwa Nam, Youn‐Joo An

Bibliographic record

VenueEnvironmental Engineering Research · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Soil, Plant Science
Canadian institutionsnot available
FundersMinistry of Environment
KeywordsEnvironmental scienceCadmiumRisk assessmentAgricultureEuropean commissionEcosystemProbabilistic risk assessmentEnvironmental protectionEnvironmental resource managementEcologyBusinessEuropean unionBiology

Abstract

fetched live from OpenAlex

For effective and efficient environmental management, developed countries, such as the Netherlands, UK, Australia, Canada, and United States apply ecological risk assessment, and they have an autonomous risk assessment methodology to protect native receptors.In this study, soil ecological protective concentration (EPC) of cadmium in Korea was derived using Korean ecological risk assessment methodology.The soil EPC of cadmium was calculated using probabilistic ecological risk assessment based on species sensitivity distribution.The soil EPC was calculated according to land use for residential/agricultural and industrial/commercial purposes.The chronic soil EPCs for residential/agricultural and industrial/commercial lands were derived to be 1.58 and 9.60 mg/kg, respectively.These values were similar to soil EPC of European Commission, the Netherlands, UK, and Canada.However, these values were lower than the established Korean soil standard, because the current soil standard was based on human risk.Therefore, the impact on an ecosystem when establishing environmental standard should be considered.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.020
GPT teacher head0.217
Teacher spread0.197 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations3
Published2013
Admission routes1
Has abstractyes

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