Toward a Global Network for Persistent Organic Pollutants in Air: Results from the GAPS Study
Bibliographic record
Abstract
The Global Atmospheric Passive Sampling (GAPS) study aims to demonstrate the feasibility of using passive samplers to assess the spatial distribution of persistent organic pollutants on a worldwide basis. The GAPS network includes more than 40 sites on 7 continents, mainly in background locations, with some representation of urban and agricultural areas. Here we present concentrations of organochlorine pesticides (OCPs), polychlorinated biphenyls (PCBs), and polybrominated diphenyl ethers (PBDEs) in polyurethane foam disk samplers, deployed from December 2004 to March 2005. Legacy OCPs such as alpha-HCH (hexachlorocyclohexane), Chlordanes (trans- and cis-Chlordane and trans-Nonachlor), Dieldrin, and dichlorodiphenyltrichloroethane isomers were detected at most sites with some high values that may be related to possible continued use and/or re-emissions related to historic use. Geometric mean (GM) air concentrations (pg/m3) were: 8.5 for sigmaHCH (sum of alpha- and gamma-isomers), 2.6 for sigmaChlordanes, 0.8 for Dieldrin, and 0.8 for p,p'-DDE. Current-use pesticides such as gamma-HCH (lindane) and especially Endosulfan I exhibited more variable and higher concentrations with GMs of 5 and 58, respectively. PCBs and PBDEs were elevated at urban/suburban sites consistent with their historical use pattern. GM concentrations (pg/m3) were 17 for PCBs and 4 for PBDEs. Sampling under GAPS will continue and will eventually allow seasonality effects and longer-term temporal and spatial trends to be evaluated.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".