A NUMERICAL ASSESSMENT OF IMPACT OF NEAR-SOURCE, LOCAL SOURCE AND REEMISSION ON THE BUDGET OF?-HEXACHLOROCYCLOHEXANE OVER THE GREAT LAKES AND ST. LAWRENCE ECOSYSTEM
Bibliographic record
Abstract
Presence of g-HCH in the Arctic and the Great Lakes ecosystem owes essentially to the atmospheric transport following its application to agricultural lands. Deposition of pesticides to the Great Lakes is also thought to have significant contributions from local sources and long-range transport over regional and even global scales. After ban in the United States in the 1980s, major sources of g-HCH in North America have been identified only in Canada where g-HCH is still used as a pesticide for treatment of canola and corn seeds (Waite et al., 1999; Poissant and Koprivnjak, 1996). In the last ten years, the Prairie Provinces canola fields (Saskatchewan, Alberta and Manitoba) of Canada have become the largest source of g-HCH in North America. Concern is raised for the impact of g-HCH application in Canada on the Great Lakes due to g-HCH’s sufficient toxicity and presence in water, sediments and aquatic biota of the Great Lakes ecosystem. The consideration of global scale long-range transport may help to distinguish important pathways of pesticides, but the contribution from trans-boundary transport in such a scale to pesticide distribution is difficult to assess and may not be significant. In reality, near-and local-sources, if they exist, always dominate the magnitude and distribution of concentration of a pesticide. In this study, an attempt is made to use a coupled atmospheric dispersion and soil-
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".