Relative contributions of gaseous oxidized mercury and fine and coarse particle‐bound mercury to mercury wet deposition at nine monitoring sites in North America
Bibliographic record
Abstract
Abstract Relative contributions to mercury wet deposition by gaseous oxidized mercury (%GOM) and fine and coarse particle‐bound mercury (%FPBM and %CPBM) were estimated making use of monitored FPBM air concentration and mercury wet deposition at nine North American locations. Scavenging ratios of particulate inorganic ions (K+ and Ca2+, Mg2+ and Na+) were used as a surrogate for those of FPBM and CPBM, respectively. FPBM and CPBM were estimated to contribute 8–36% and 5–27%, respectively, depending on the location, to total wet deposition. The rest of the 39–87% was attributed to the contribution of GOM. The average %GOM, %FPBM and %CPBM among all locations were 65%, 17%, and 18%, respectively. The relative distributions of %GOM, %FPBM, and %CPBM were influenced by Hg(II) gas‐particle partitioning, urban site characteristics, and precipitation type. At the regional scale, %GOM dominated over %FPBM and %CPBM. However, the sum of FPBM and CPBM contributed to nearly half of the total Hg wet deposition in urban areas, which was greater than other site categories and is attributed to higher FPBM air concentrations. At four locations, %FPBM exceeded %GOM during winter in contrast to summer, suggesting the efficient snow scavenging of aerosols. The results from this study are useful in improving mercury transport models since most of these models do not estimate CPBM, but frequently use monitored mercury wet deposition data for model evaluation.
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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 | 0.001 | 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".