Seasonal and spatial variations in the chemistry of a High Arctic supraglacial snow cover
Bibliographic record
Abstract
Abstract This paper describes the physical and chemical properties of the snow- pack on John Evans Glacier, Ellesmere Island, Canadian Arctic Archipelago, and investigates the controls on snowpack solute concentrations and atmospheric deposition. The snowpack contains three layers that are traceable across the whole glacier. These represent fall accumulation that has been metamorphosed to depth hoar, winter accumulation mixed with snow reworked by wind from the underlying depth hoar, and spring accumulation mixed with wind-reworked snow. The seasonal cycle in snow chemistry closely reflects changes in the composition of the atmospheric aerosol at Alert, with some modification of NO3− concentrations by post-depositional processes. Mean water-weighted solute concentrations in the snowpack are largely independent of accumulation, while atmospheric deposition tends to increase with accumulation. This suggests that, for most species, wet deposition is the dominant depositional process throughout the year. However, concentrations of Ca2+ and K+ increase with both accumulation and elevation, implying an enhanced input from dry deposition of soil dust above 800 m elevation. Concentrations of SO42− are inversely related to accumulation, especially in the winter layer, suggesting a significant input from non-precipitating events, such as dry deposition or riming, during this period of very limited snowfall.
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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".