Accuracy, Conservatism and Parsimony of Three Vapour Intrusion Models Used in Sweden
Bibliographic record
Abstract
This study presents an evaluation of three screening-level models, namely the Dilution Factor (DF) model from 1996, the update version from 2005, as well as the Johnson and Ettinger model (JEM) from 1997, that are applied within the frameworks for contaminated land management (CLM) in Sweden. This evaluation applies, besides a deterministic approach (point estimate), a probabilistic assessment plus sensitivity analysis. The latter approach allows the models to be ranked according to conservatism, accuracy and parsimony by contrasting predicted and observed soil and indoor air concentrations for two contaminants (benzene and ethylbenzene), as to determine their suitability for application within CLM. The results and conclusions from this study suggest that the most accurately model for predicting the soil and indoor concentration is the JEM followed by the DF 2005 and 1996. Predictions of the soil air concentration are primarily driven by variation in physico-chemical parameters. The variation in indoor air concentration by physico-chemical and/or soil parameters for the DF models, while for the JEM soil parameters dominate. The deterministic analysis showed that default parameter values could be revised as to increase the conservatism, and be closer to the probabilistic 95-percentile predicted indoor air concentration. The DF 1996 model includes a limited number of parameters, and this analysis shows that a model with more parameters is more accurate, and less conservative. The DF 2005 seems to be the most parsimonious model as it is accurate, sufficiently conservative, and has 14 parameters, whereas the DF 1996 with 9 parameters is the most conservative and the JEM with 27 parameters the most accurate with an increased probability to produce false negative predictions. For the latter some of the dominant parameters cannot easily be measured on site.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".