Soil contamination sampling intensity: determining accuracy and confidence using a Monte Carlo simulation
Bibliographic record
Abstract
Soil pollution is an extensive global problem, and effective management depends on accurate characterization and mapping of the extent of soil contamination. The objectives of this study were to determine the accuracy of different sampling intensities and the optimal number of samples required to minimize remediation project costs. To determine the accuracy associated with different sampling intensities, a Monte Carlo simulation was conducted. A simulated contaminant plume was created based on inverse distance weighting, kriging, or multivariate adaptive regression splines. Different sampling intensities, with grid spacing ranging from approximately 10% to 50% of the site extent, were used to generate a plume map using random forest model with a Euclidean distance matrix as predictors. The relative error was then determined as part of a Monte Carlo simulation that ran 10 000 simulations for each grid intensity for a total of 90 000 simulations. The optimal number of samples was determined based on economic factors, and the error functions generated with the Monte Carlo simulations. Average error ranged from 57% for 25 data points to 5% for 2800 data points. The 90th percentile error ranged from 100% to 0.3% for the sample data point range. Based on these results, the optimal number of samples, depending on pricing, ranged from 31 samples for a 10 m3 contaminant plume to 3475 samples for a 10 000 m3 soil contaminant plume.
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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 | 0.031 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".