Prairie and arctic areal snow cover mass balance using a blowing snow model
Bibliographic record
Abstract
Algorithms to calculate the threshold wind speed and the effect of exposed vegetation on saltation and to describe vertical profiles of humidity in blowing snow, permit the calculation of point blowing snow transport and sublimation fluxes using standard meteorological and landcover data or simple interfaces with climate models. Blowing snow transport and sublimation fluxes can be upscaled to calculate open environment snow accumulation by accounting for their variability over open snow fields, increase in transport and sublimation with fetch, and the effect of exposed vegetation on partitioning the shear stress available to drive transport. Blowing snow fluxes scaled in this manner were used to calculate snow mass balance and to simulate seasonal snow accumulation at a southern Saskatchewan prairie and an arctic site. Field measurements at these sites indicated that from 48% to 58% of snowfall was removed by blowing snow before melt began. Simulations suggest that the ratios of snow removed and sublimated by blowing snow to that transported were 2∶1 and 1∶1 at the prairie and arctic sites respectively. The resulting methodology was capable of estimating winter season mass balances for these snowpacks that compared well with snowfall and snow accumulation measurements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".