Vegetation and Topographic Control of Wind-Blown Snow Distributions in Distributed and Aggregated Simulations for an Arctic Tundra Basin
Bibliographic record
Abstract
A finescale model of blowing snow is used to simulate the characteristics of snow cover in a low-Arctic catchment with moderate topography and partial shrub cover. The influence of changing shrub characteristics is investigated by performing a sequence of simulations with varying shrub heights and coverage. Increasing shrub height gives an increase in snow depth within the shrub-covered areas, up to a limit determined by the supply of falling and blowing snow, but increasing shrub coverage gives a decrease in snow depths within shrubs as the supply of blowing snow imported from open areas is reduced. A simulation of snow redistribution over the existing topography without any shrub cover gives much greater accumulations of snow on slopes in the lee of the prevailing wind than on windward slopes; in contrast, shrubs are able to trap snow on both lee and windward slopes. A spatially aggregated, or tiled, model is developed in which snow is relocated by wind transport from sparsely vegetated tiles to more densely vegetated tiles. The vegetation distribution is not specified, but the simulation is parameterized using average fetch lengths along the major transport axis. The aggregated model is found to be capable of matching the average snow accumulation in shrub and open areas predicted by the distributed model reasonably well but with much less computational cost.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".