Conditions controlling atmospheric pollutant deposition via snowpack
Bibliographic record
Abstract
Solid precipitation represents a potentially important addition to other measures of deposition. However, an accurate estimate of snowfall amount and pollutant loading is not a trivial matter. There are obvious distinctions between regular precipitation collection and snowpack sampling that represent the cumulative chemistry of bulk deposition. The main goal is to show the most important processes and factors that may influence the rate and magnitude of pollutants deposition affected by the snowfall and snow cover: atmospheric pollutant enhancement of snowfall, pollutants deposition at snow cover surface, drifting and blowing snow, formation of the snow cover and its internal changes, as well as pollutants flow through the snowpack. These phenomena lead to continuous changes in the chemistry of the snow cover and the deposition calculated on the basis of pollutants concentrations in daily portions of atmospheric precipitation. The real deposition released from snowpack is strictly related to the number and depth of thaw episodes. If the amount of stored pollutants is large, first portions of ablation water flushing from the snowpack can carry the load of pollutants, and potentially affecting the environment in a detrimental way. Igneous bedrock is especially sensitive to acidic ions because of its low buffering capacity.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".