Modelling Arsenic and Lead Surface Soil Concentrations using Land Use Regression
Bibliographic record
Abstract
Land Use Regression (LUR) models are increasingly used in environmental and exposure assessments to predict the concentration of contaminants in outdoor air. We explore the use of LUR as an alternative to more complex models to predict the concentration of metals in surface soil. Here, we used 55 soil samples of As and Pb collected in 1996 across British Columbia (BC), Canada by the Ministry of Environment. Predictor variables were derived for each sample site using Geographic Information System (GIS). For As (R2 = 0.44), the resulting linear regression model includes the total length of roads (m) within 25 km, and bedrock geology. For the Pb model (R2 =0.78), the predictor variables are the total surface area of industrial land use (m2) within 5 km , the emissions of Pb (t) within 10 and 25 km, and the presence of closed mines within 50 km. The study proposes that LUR can reasonably predict the concentrations of As and Pb in surface soil over large areas.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".