Spatial Trends, Sources, and Air–Water Exchange of Organochlorine Pesticides in the Great Lakes Basin Using Low Density Polyethylene Passive Samplers
Bibliographic record
Abstract
Polyethylene passive samplers were deployed during summer and fall of 2011 in the lower Great Lakes to assess the spatial distribution and sources of gaseous and freely dissolved organochlorine pesticides (OCPs) and their air-water exchange. Average gaseous OCP concentrations ranged from nondetect to 133 pg/m(3). Gaseous concentrations of hexachlorobenzene, dieldrin, and chlordanes were significantly greater (Mann-Whitney test, p < 0.05) at Lake Erie than Lake Ontario. A multiple linear regression implied that both cropland and urban areas within 50 and 10 km buffer zones, respectively, were critical parameters to explain the total variability in atmospheric concentrations. Freely dissolved OCP concentrations (nondetect to 114 pg/L) were lower than previously reported. Aqueous half-lives generally ranged from 1.7 to 6.7 years. Nonetheless, concentrations of p,p'-DDE and chlordanes were higher than New York State Ambient Water Quality Standards for the protection of human health from the consumption of fish. Spatial distributions of freely dissolved OCPs in both lakes were influenced by loadings from areas of concern and the water circulation patterns. Flux calculations indicated net deposition of γ-hexachlorocyclohexane, heptachlor-epoxide, and α- and β-endosulfan (-0.02 to -33 ng/m(2)/day) and net volatilization of heptachlor, aldrin, trans-chlordane, and trans-nonachlor (0.0 to 9.0 ng/m(2)/day) in most samples.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".