How relevant is the deposition of mercury onto snowpacks? – Part 1: A statistical study on the impact of environmental factors
Bibliographic record
Abstract
Abstract. A portion of the highly toxic methylmercury that bioaccumulates in aquatic life is created from mercury entering bodies of water with snowpack meltwater. To determine the importance of meltwater as a source of aquatic mercury, it is necessary to understand the environmental processes that govern the behavior of snowpack-related mercury. In this study we investigate relationships among 5 types of snowpack-related mercury observations and 20 model environmental variables. The observation types are the 24-h fractional loss of mercury from surface snow, and the concentrations of mercury in surface snow, seasonal snowpacks, the snowpack meltwater's ionic pulse, and long-term snowpack-related records. The model environmental variables include those related to atmospheric mercury, insolation, wind, atmospheric stability, snowpack physical characteristics, atmospheric pressure, and solid precipitation. Bivariate and multiple linear regressions were performed twice for each mercury observation type: once with all observations, and once excluding observations from locations where the snowpack's burden of oxidizing and stabilizing halogens is known or presumed to affect snowpack mercury. Since no observations from long-term snowpack-related records were considered affected by halogens, this group of observations was included with the sets of uninfluenced observations and was not discussed with the complete, original sets of observations. When all observations are included, only 37% of their variability can be explained, on average, with significance confidence levels averaging 81%; a separate regression model predicts each mercury observation type. Without the influence of halogens, the regression models are able to explain an average of 79% of the observations' variability with significance confidence levels averaging 97%. The snowpack-related mercury observations are most strongly controlled by the dry and wet depositions of oxidized mercury, and by precipitation. Mercury deposited through wet processes is more strongly retained by snowpacks than mercury deposited through dry processes. Revolatilization of mercury deposited through wet processes may be inhibited through burial by fresh snowfalls and/or by its more central location, compared to that of mercury deposited through dry deposition, within snowpack snow grains. The two depositions of oxidized mercury together explain 84% of the variability in observed concentrations of mercury in surface snow, 52% of the variability of observed concentrations of mercury in seasonal snowpacks and their meltwater's ionic pulse, and only 20% of the variability of observed concentrations of mercury in long-term snowpack-related records; other environmental controls seemingly gain in relevance as time passes. The concentration of mercury in long-term records is apparently primarily affected by latitude; both the primary sources of anthropogenic mercury and the strong upper-level zonal winds are located in the midlatitudes.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".