Bibliographic record
Abstract
Considering the importance of snow hydrology for water supply in the Cana-dian Prairies, and the impact of blowing snow processes on the amount of snow available for spring melt, there is a need to develop models of blowing snow trans-port and sublimation in alpine terrain. A distributed model of blowing snow model (DBSM) was developed by Essery [2] based on MS3DJH/3R terrain wind flow model and the Prairie Blowing Snow Model (PBSM,[9]). This model applied to rolling topography (Trail Valley in the arctic tundra) showed good results. In our study, an attempt was made to run DBSM over steep mountain topography in Marmot Creek Basin, Canadian Rocky Mountains and to assess its performance. Breaking the assumption of rolling topography, the accuracy of the linear wind flow model is challenged. Using LiDar data for elevation and snow depth and observations from different weather station in the basin, the behaviour of the wind flow model for calculating blowing snow was tested. Although the model must show serious deficiencies in modelling wind on steep slopes, some observed features of redistributed snow covers might be reproduced. As an alternative, a commercial wind flow model (Windsim) using a 3D Reynolds averaged Navier-Stokes equations and the k − ǫ turbulence model was coupled to DBSM. This model showed promising results in the complex moun-tain topography. Nevertheless, the limited number of cells is too coarse to predict complex wind regimes, which are affected by fine scale topographic configurations. Both model do not reproduce the features of wind blowing over ridges. So, eventually, this particular case of blowing snow process is studied through a two dimensional approach with Ansys CFD program. Au vu de l’importance de l’hydrologie de la neige pour les ressources en eau des Prairies canadiennes et de l’impact du vent sur la redistribution de ce bien précieux, il existe un besoin fort de développement de modèles des processus éoliens de transport et de sublimation de la neige. Un modèle distribué simulant ce phénomène a été développé par Essery [2]. Ce modèle basé sur le “Prairie
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 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".