Modeling the Uptake of Neutral Organic Chemicals on XAD Passive Air Samplers under Variable Temperatures, External Wind Speeds and Ambient Air Concentrations (PAS-SIM)
Bibliographic record
Abstract
The main objective of this study was to evaluate the performance and demonstrate the utility of a fugacity-based model of XAD passive air samplers (XAD-PAS) designed to simulate the uptake of neutral organic chemicals under variable temperatures, external wind speeds and ambient air concentrations. The model (PAS-SIM) simulates the transport of the chemical across the air-side boundary layer and within the sampler medium, which is segmented into a user-defined number of thin layers. Model performance was evaluated using data for polychlorinated biphenyls (PCBs) and polycyclic aromatic hydrocarbons (PAHs) from a field calibration study (i.e., active and XAD-PAS data) conducted in Egbert, Ontario, Canada. With some exceptions, modeled PAS uptake curves are in good agreement with the empirical PAS data. The results are highly encouraging, given the uncertainty in the active air sampler data used as input and other uncertainties related to model parametrization (e.g., sampler-air partition coefficients, the influence of wind speed on sampling rates). The study supports the further development and evaluation of the PAS-SIM model as a diagnostic (e.g., to aid interpretation of calibration studies and monitoring data) and prognostic (e.g., to inform design of future passive air sampling campaigns) tool.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".