Field Evaluation of a Flow-Through Sampler for Measuring Pesticides and Brominated Flame Retardants in the Arctic Atmosphere
Bibliographic record
Abstract
A flow-through sampler (FTS) was codeployed with a super high volume active sampler (SHV) between October 2007 and November 2008 to evaluate its ability to determine the ambient concentrations of pesticides and brominated flame retardants in the Canadian High Arctic atmosphere. Nine pesticides and eight flame retardants, including three polybrominated diphenyl ether (PBDE) replacement chemicals, were frequently detected. Atmospheric concentrations determined by the two systems showed good agreement when compared on monthly and annually integrated time scales. Pesticide concentrations were normally within a factor of 3 of each other. The FTS tended to generate higher PBDE concentrations than the SHV presumably because of the entrainment of blowing snow/ice crystals or large particles. Taking into account uncertainties in analytical bias, sample volume, and breakthrough estimations, the FTS is shown to be a reliable and cost-effective method, which derives seasonally variable concentrations of semivolatile organic trace compounds at extremely remote locations that are comparable to those obtained by conventional high volume air sampling. Moreover, the large sampling volumes captured by the FTS make it suitable for the screening of new and emerging chemicals in the remote atmosphere where concentrations are usually low.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".