Variability of North American regulatory guidance for heavy metal contamination of residential soil
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".