Trend and climate signals in seasonal air concentration of organochlorine pesticides over the Great Lakes
Bibliographic record
Abstract
Following worldwide bans or restrictions, the atmospheric level of many organochlorine pesticides (OCPs) over the Great Lakes exhibited a decreasing trend since the 1980s in various environmental compartments. Atmospheric conditions also influence variation and trend of OCPs. In the present study a nonparametric Mann‐Kendall test with an additional process to remove the effect of temporal (serial) correlation was used to detect the temporal trend of OCPs in the atmosphere over the Great Lakes region and to examine the statistical significance of the trends. Using extended time series of measured air concentrations over the Great Lakes region from the Integrated Atmospheric Deposition Network, this study also revisits relationships between seasonal mean air concentration of OCPs and major climate variabilities in the Northern Hemisphere. To effectively extract climate signals from the temporal trend of air concentrations, we detrended air concentrations through removing their linear trend, which is driven largely by their respective half‐lives in the atmosphere. The interannual variations of the extended time series show a good association with interannual climate variability, notably, the North Atlantic Oscillation (NAO) and the El Niño–Southern Oscillation. This study demonstrates that the stronger climate signals can be extracted from the detrended time series of air concentrations of some legacy OCPs. The detrended concentration time series also help to interpret, in addition to the connection with interannual variation of the NAO, the links between atmospheric concentrations of OCPs and decadal or interdecadal climate change.
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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 | 0.000 | 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".