Drifting snow measurements over an instrumented mountainous site : improvement of numerical model input parameters
Bibliographic record
Abstract
Blowing snow may occur both in mountain ranges and on flat terrain, creating cornices, slabs, or snowdrifts. As a consequence, the probability of avalanche occurrence can be totally modified, roads and buildings can be obstructed and drivers' visibility can be dramatically reduced. Therefore, the prediction and control of drift patterns are of real importance. Different blowing-snow studies were conducted using either physical or numerical modeling. The numerical model currently used in Cemagref is NEMO. Although its predictions correctly reproduce the windward and leeward accumulations\naround fences in wind tunnels, the computation results were often less conclusive when compared to those of field experiments. Indeed, accurate evaluations of the input parameters needed for the numerical model remain an open question. At the present time for our numerical model the estimation of roughness, blowing snow mass fluxes and wind profiles is mainly obtained from empirical relations determined by\nPomeroy and Gray on flat areas in Canada. As the topography and type of snow could be quite different in the Alps, further experimental research is needed before using such formulae. To obtain the required field data (roughness, friction speed, threshold friction speed, flowing snow mass fluxes profiles), specific optical and acoustic instruments (e.g., SPC and Flowcapt, respectively) and a 10 meter-mast with 6 anemometers, 3 temperature sensors and a depth sensor were set up on our experimental site Col Du Lac Blanc (2700 m) in the Alps. New data obtained during winter 2007-2008\nare compared with empirical formulae and past experimental data.
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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) | 0.000 | 0.000 |
| 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 | 0.001 | 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".