Sawlem: Slab and Weak Layer Evolution Model
Bibliographic record
Abstract
ABSTRACT: Snowpack evolution models developed in Europe require meteorological and radiation instrumentation rarely used in North America. In contrast, SAWLEM uses a spreadsheet to model the evolution of persistent weak layers and the overlying dry slabs using weekly manual snow profiles in a study plot and daily measurements or long-term average values of snowfall. The shear strength of a persistent weak layer, dependent on the grain type, is estimated based on parameters such as slab load, grain size, thickness of the weak layer, thickness of the slab, snowpack height and temperature just below the weak layer from a detailed manual profile in a study plot. The calculated shear strength is adjusted daily based on the most recently measured snow profile parameters and recent snowfall rates or average long-term snowfall values in the area according to previously published empirical models. Slab load, ski penetration and skier stress at the base of the slab and subsequently skier stability indices based on the ratio of shear strength to shear stress such as Sk38 are calculated. For daily calculations of Sk38 the slab settles according to the long-term average and the 24 hour snowfall is added to the slab thickness in order to calculate the slab load on days without snowprofile observations. We summarize comparisons of the estimated and measured shear strength as well as correlations of Sk38 with skier-triggered avalanche activity in the Columbia Mountains.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".