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Record W7100888665

Blowing snow redistribution

2009· article· en· W7100888665 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
Fundersnot available
KeywordsSnowTerrainTurbulenceElevation (ballistics)Wind speedArcticSnowmeltSnow fieldWinter storm
DOInot available

Abstract

fetched live from OpenAlex

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

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.018
GPT teacher head0.216
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2009
Admission routes1
Has abstractyes

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