Relocation of Major Ions in Snow along the Tundra-Taiga Ecotone
Bibliographic record
Abstract
The chemistry of seasonal snowcovers north of Inuvik, Northwest Territories, Canada was stratified by biophysical landscape. In this region, deposition of ions in winter occurs largely through the redistribution of wind-blown snow with accumulations in forest-edges and valley sides 8 to 12 times that of the open tundra. While dominated by this snow redistribution, the loading of most ions, except for SO^^-, does not scale exactly with that of snow, there being several mechanisms by which ion concentrations become relatively enriched or depleted in various landscape units. Vaporisation during temperature-gradient metamorphism in shallow-snow and uptake during either photochemical reac-tions or gaseous scavenging to well-exposed snow transformed concentrations of NOs- by 50 %. Dry deposition of aerosols to forested terrain and valley bottoms enriched C1-, ~ a +, M ~ ~ +, K+ and ca2+ concentrations up to more than two-fold, however scavenging of aerosols to blowing snow particles contri-buted an additional 40 % to the sea-salt enrichment and 20 % to the ca2+ enrichment in wind-blown treeline forests. It is concluded that central measure-ments of snow chemistry in the Arctic cannot be reliably extrapolated without reference to changes caused by over-winter physical and chemical metamorphic processes. Associating the physicaVchemica1 changes with readily identifiable Arctic landscape units suggests a simple and robust method for spatial extrapo-lation.
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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 | 0.001 | 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".