Temporal Trends of Organochlorine Pesticides in the Canadian Arctic Atmosphere
Bibliographic record
Abstract
Temperature normalization (TN), multiple linear regression (MLR), and digital filtration (DF) were used to analyze the temporal trends of an atmospheric dataset on organochlorine pesticides (OCs) collected at the Canadian high arctic site of Alert, Nunavut. Details of these techniques have been presented before (Environ. Sci. Technol. 2001, 35, 1303-1311). Both the TN and DF methods revealed that the majority of OC pesticides declined over the 5 years of study, except endosulfan I and several of the pesticide metabolites, including dieldrin and p,p'-DDE. In comparison to studies conducted in the Great Lakes, atmospheric levels in the Arctic were less dependent on temperature, although seasonal variations were apparent. Generally, levels in the winter were lower than during the rest of the year. A notable exception was p,p'-DDE. Many compounds also showed a second minimum in concentrations during June/July and possible explanations are presented to account for this. The estimated first order half-lives for the decline in OC concentrations were generally found to be comparable or slightly longer than those obtained at temperate locations, with the exception of alpha-HCH, which displayed a much longer half-life in the Arctic (approximately 17 yrs). Sporadic increases in heptachlor as well as increases in the ratio of trans- to cis-chlordane suggest episodic input of chlordanes between 1995 and 1997, especially during the winter.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".