Modeling of temporal patterns and sources of atmospherically transported and deposited pesticides in ecosystems of concern: A case study of toxaphene in the Great Lakes
Bibliographic record
Abstract
Abstract Pesticides have adverse effects on human health and the environment and can be transported through the atmosphere from application sites and deposited to sensitive ecosystems. This study applies a comprehensive multimedia regional pesticide fate and chemical transport modeling system that we developed to investigate the atmospheric transport and deposition of toxaphene to the Great Lakes. Simulated results predict a significant amount of toxaphene (~350 kg) being transported through the atmosphere and deposited into the Great Lakes in the simulation year. Results also show that U.S. residues and global background are major sources to toxaphene deposition into the Great Lakes and atmospheric concentrations in the region. While the U.S. residues are the dominant source in warm months, the background dominates during winter months. In addition, different sources have different influences on the individual Great Lakes due to their proximity and relative geographical positions to the sources; U.S. residues are the dominant source to Lakes Ontario, Erie, Huron, and Michigan, but they are a much less important source to Lake Superior. These results shed light on the mystery that observed toxaphene concentrations in Great Lakes' lake trout and smelt declined between 1982 and 1992 in four of the Great Lakes except Lake Superior. While monthly total depositions to Lakes Ontario, Erie, Huron, and Michigan have clear seasonal variability with much greater values in April, May, and June, monthly total depositions to Lake Superior are more uniformly distributed over the year with comparatively greater levels in cold months.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".