Atmospheric mercury speciation and mercury in snow over time at Alert, Canada
Bibliographic record
Abstract
Abstract. Ten years of atmospheric mercury speciation data and 14 years of mercury in snow data from Alert, Nunavut, Canada, are examined. The speciation data, collected from 2002 to 2011, includes gaseous elemental mercury (GEM), particulate mercury (PHg) and reactive gaseous mercury (RGM). During the winter-spring period of atmospheric mercury depletion events (AMDEs), when GEM is close to being completely depleted from the air, the concentration of both PHg and RGM rise significantly. During this period, the median concentrations for PHg is 28.2 pgm−3 and RGM is 23.9 pgm−3, from March to June, in comparison to the annual median concentrations of 11.3 and 3.2 pgm−3 for PHg and RGM, respectively. In each of the ten years of sampling, the concentration of PHg increases steadily from January through March and is higher than the concentration of RGM. This pattern begins to change in April when the levels of PHg peak and RGM begin to increase. In May, the high PHg and low RGM concentration regime observed in the early spring undergoes a transition to a regime with higher RGM and much lower PHg concentrations. The higher RGM concentration continues into June. The transition is driven by the atmospheric conditions of air temperature and particle availability. Firstly, a high ratio of the concentrations of PHg to RGM is reported at low temperatures which suggests that oxidized gaseous mercury partitions to available particles to form PHg. Prior to the transition, the median air temperature is −24.8 °C and after the transition the median air temperature is −5.8 °C. Secondly, the high PHg concentrations occur in the spring when high particle concentrations are present. The high particle concentrations are principally due to Arctic haze and sea salts. In the snow, the concentrations of mercury peak in May for all years. Springtime deposition of total mercury to the snow at Alert peaks in May when atmospheric conditions favour higher levels of RGM. Therefore, the conditions in the atmosphere directly impact when the highest amount of mercury will be deposited to the snow during the Arctic spring.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".