Atmospheric Sampling of Persistent Organic Pollutants: Needs, Applications and Advances in Passive Air Sampling Techniques
Bibliographic record
Abstract
There are numerous potential applications for validated passive sampling techniques to measure persistent organic pollutants (POPs) in the atmosphere, but such techniques are still in their infancy. Potential uses include: monitoring to check for regulatory compliance and identification of potential sources; cheap/efficient reconnaissance surveying of the spatial distribution of POPs; and deployment in studies to investigate environmental processes affecting POP cycling. This article reviews and discusses the principles and needs of passive sampling methodologies. The timescales required for analytical purposes and for the scientific objectives of the study are critical in the choice and design of a passive sampler. Some techniques may operate over the timescales of hours/days, others over weeks/months/years. We distinguish between approaches based on "kinetic uptake" and "equilibrium partitioning". We highlight potentially useful techniques and discuss their potential advantages, disadvantages, and research requirements, drawing attention to the urgent need for detailed studies of sampler performance and calibration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".