Evaluating the PAS-SIM model using a passive air sampler calibration study for pesticides
Bibliographic record
Abstract
The main objective of this study was to evaluate the performance of a model for simulating the uptake of various pesticides on passive air samplers (PAS). From 2006-2007 a series of PAS using XAD-resin were deployed at Egbert, a rural agricultural site in southern Ontario, Canada, to measure the uptake of pesticides for time periods ranging from two months to one year. A continuous increase in sequestered amounts was observed for most pesticides, except for trifluralin and pendimethalin, which could conceivably be subject to substantial degradation inside the sampler. Continuous low-volume active air samples taken during the same period, along with data on weather conditions, allowed for the simulation of the uptake of the pesticides using the model (PAS-SIM). The modelled accumulation of pesticides on the PAS over the deployment period was in good agreement with the experimental data in most cases (i.e., within a factor of two) providing insight into the uptake kinetics of this type of sampler in the field. Passive sampling rates (PSR, m(3) d(-1)) were determined from the empirical data generated for this study using three different methods and compared with the PSRs generated by the model. Overall, the PAS-SIM model, which is capable of accounting for the influence of temperature and wind variations on PSRs, provided reasonable results that range between the three empirical approaches employed and well-established literature values. Further evaluation and application of the PAS-SIM model to explore the potential spatial and temporal variability in PAS uptake kinetics is warranted, particularly for established monitoring sites where detailed meteorological data are more likely to be available.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 | 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".