Modeling the Atmospheric Transport and Deposition of PCDD/F to the Great Lakes
Bibliographic record
Abstract
Atmospheric deposition is a significant loading pathway for polychlorinated dibenzo-p-dioxins and dibenzofurans (dioxin) to the Great Lakes. An innovative approach using NOAA's HYSPLIT atmospheric fate and transport model was developed to estimate the 1996 dioxin contribution to each lake from each of 5,700 point sources and 42,600 area sources in a U.S./Canadian air emissions inventory. These unusually detailed source-receptor modeling results show that deposition to each lake arises from a broad geographical region, with significant contributions from up to 2,000 km away. The source categories contributing most significantly to 1996 dioxin deposition appear to be municipal waste incineration, iron sintering, medical waste incineration, and cement kilns burning hazardous waste. Model-predicted air concentrations and deposition fluxes were consistent with ambient measurement data, within the uncertainties in each, but there may be a moderate tendency toward underestimation using midrange emissions estimates. The most likely reason for this tendency appears to be missing or underestimated emissions sources, but in-situ atmospheric formation of octachlorinated dibenzo-p-dioxin (OCDD) and heptachlorinated dibenzo-p-dioxin (HpCDD) may have also contributed. Despite uncertainties, the findings regarding the relative importance of different sources types and source regions appear to be relatively robust and may be useful in prioritizing pollution prevention efforts.
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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 | 0.001 | 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".