Using Passive Air Samplers To Assess Urban−Rural Trends for Persistent Organic Pollutants. 1. Polychlorinated Biphenyls and Organochlorine Pesticides
Bibliographic record
Abstract
Passive air samplers were used to investigate urban-rural differences of polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs) over an integrated time period. Samplers consisting of polyurethane foam (PUF) disks and semi-permeable membrane devices (SPMDs) were housed in protective chambers and deployed at six sites for a 4 month duration in the summer of 2000. The sampling transect originated in downtown Toronto and extended approximately 75 km northward into a rural region. Results for the two types of samplers agreed well with one another. Higher blank levels were encountered for the SPMDs, especially for the OCPs, whereas blanks were very low for the PUF disks. Passive sampler-derived air concentrations were consistent with previous measurements of PCBs and OCPs in the region. The largest urban-rural gradient was observed for PCBs (approximately 5-10 times). Chlordanes also showed an urban-rural gradient, possibly reflecting past usage of chlordane on residential lawns and emissions from treated house foundations. Other OCPs exhibited a rural-urban gradient (dieldrin, endosulfan 1, and DDT isomers), which was attributed either to off-gassing from previously treated agricultural soils (dieldrin and DDTs) or to continued usage in agriculture (endosulfan 1). The results of this study demonstrated the feasibility of using such devices to determine air concentrations of persistent organic pollutants (POPs) and to assess their spatial distribution for time-integrated samples. Data such as this is essential for: model validation and for process research and addressing international monitoring strategies on POPs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".