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Record W2059198482 · doi:10.1139/s06-019

Variability of North American regulatory guidance for heavy metal contamination of residential soil

2006· article· en· W2059198482 on OpenAlexvenueno aff
Aaron A. Jennings, Elijah J. Petersen

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsnot available
FundersCleveland State UniversityNational Science Foundation
KeywordsEnvironmental remediationEnvironmental scienceSoil waterSpatial variabilityContaminationSoil contaminationSoil remediationHazard analysisHazardNonparametric statisticsStatisticsSoil scienceMathematicsEcologyEngineering

Abstract

fetched live from OpenAlex

The health implications and remediation obligations of contaminated urban soils are significant issues in many North American cities. Remediation guidance for soils is evolving and shifting from values based on geochemical background approximations to values based on health risks. This analysis examines residential-soil guidance variability among the states and provinces of North America for Cd, Cr, Cu, Ni, Pb, and Zn. For some metals, values vary by as much as 5 orders of magnitude. Nonparametric comparisons are used to illustrate the degree to which current standards differ and to examine the spatial distribution of differences. Parametric statistical analysis is used to examine the significance of variations, and to determine if correlations exist in the structure of guidance values. Hazard index analysis is also used to examine how guidance value differences impact site risk assessments. Results indicate that most of the current variation can be explained as plausible randomness if determining maximum acceptable soil exposures is a random process. Differences between the statistical properties of Cd, Cr, Cu, Ni, and Zn guidance and that of Pb also demonstrate the influence that national leadership can have on stabilizing guidance. Key words: heavy metals, remediation guidance values, residential soil contamination, risk analysis, hazard index analysis.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.647
Threshold uncertainty score0.511

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.004
GPT teacher head0.194
Teacher spread0.190 · 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

Citations30
Published2006
Admission routes1
Has abstractyes

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