On snow depth predictions with the Canadian land surface scheme including a parametrization of blowing snow sublimation
Bibliographic record
Abstract
A parametrization of the sublimation of blowing snow has been developed. This parametrization has been used with the Canadian Land Surface Scheme (CLASS) to compare predicted results to observed snow depth measurements at three locations: Resolute, Goose Bay and Hay River. These locations demonstrate a high, moderate, and low incidence of blowing snow, respectively, according to criteria based on temperature, snow cover, and wind speed. Substantial change due to blowing snow sublimation is seen at the Resolute location. The inclusion of blowing snow sublimation generally improves the results for 23 of the 28 winters modelled. At Goose Bay the change in modelled snow depth due to blowing snow sublimation is moderate, and at Hay River it is marginal. The average snow density at Resolute modelled by CLASS is 30% less than the average measured snow density during the same period, for air temperatures below 0°C; this is likely due to wind packing of the snow. Increasing the snow density within CLASS at Resolute and including blowing snow sublimation significantly reduce the model snow depth error. At Goose Bay and Hay River, results are complicated by the possibility of missed melting events during brief warm periods and misdiagnosed precipitation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".