Comparison, Validation, and Use of Models for Predicting Indoor Air Quality from Soil and Groundwater Contamination
Bibliographic record
Abstract
The use of models to predict indoor air quality and health risk for the soil vapor transport to indoor air pathway is commonplace; however, there is significant uncertainty surrounding processes and factors affecting this pathway, and the accuracy of models used. Available screening models were evaluated through a review of model characteristics and sensitivity, and through comparisons to measured conditions at field sites. Model simulations and comparisons to field data indicate that the vapor attenuation ratio (α) is highly sensitive to certain processes (e.g., biodegradation and ad-vection) and input parameters. Comparisons of model predicted to measured a values indicate that models based on the Johnson and Ettinger (1991) framework in most cases result in predictions that are conservative by up to one to two orders of magnitude for field sites that were assessed, providing that appropriate input parameters are used. However, for sites where the advection potential is high, these models may not be conservative. The potential for advective transport of vapors into building may be significant for sites with shallow contamination, high permeability soil and foundation and high building underpressurization. The paper concludes with possible tiered management framework for the soil vapor pathway.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".