A Local Scale Modeling Study of Mercury Depletion Event at Canadian Arctic
Bibliographic record
Abstract
A modeling study was conducted on the transformation and deposition patterns of atmospheric mercury in the Canadian Arctic. One Dimensional (1-D) local scale model was used to simulate the episodic depletions of gaseous elemental mercury (GEM) after polar sunrise at Alert, Canada. The model was developed by starting with existing meteorological model (LCM-Local Climate Model) which is coupled with Canadian Aerosol Module (CAM) and then adding modules specific to atmospheric mercury chemistry. The model is able to simulate local scale transport of mercury over the entire depth of the troposphere with a basic time step of 20 min. and incorporates current knowledge of transformation reactions of atmospheric mercury species. Three mercury species Hg(O), Hg(II) and Hg(p) were considered. The developed model was applied to a portion of the Canadian Arctic region, Alert, for the month of April 2002. The model was then evaluated by comparing model estimates of mercury species concentrations with the measurement data collected in the Canadian Arctic by Meteorological Services of Canada, Downsview, Ontario. The results from this modeling study agree reasonably well with some underestimation caused by lower conversion of gaseous elemental mercury (GEM) into reactive gaseous mercury (RGM) and subsequent conversion to total particulate mercury (TPM). A sensitivity analysis was also conducted to examine the depositions of mercury species in response to changes in ozone and soot concentrations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".