Linking the effective thermal conductivity of snow to its shear strength and density
Bibliographic record
Abstract
[1] The effective thermal conductivity of snow, keff, is a crucial climatic and environmental variable. Here, we test the intuition that keff is linked to microstructural and mechanical properties by attempting to relate keff to density ρsnow, and to shear strength σ measured with a handheld shear vane. We performed 106 combined measurements of keff, ρsnow and σ in the Alps, Svalbard, Arctic Alaska, and near the North Pole, covering essentially all snow types. We find a good correlation between keff and ρsnow which is not significantly different from that of Sturm et al. (1997). The correlation between keff and a combination of σ and ρsnow is stronger than with density alone. We propose an equation linking keff, (W m−1 K−1) ρsnow (kg m−3) and σ (Pa): keff = 7.114 10−5 ρsnow σ0.333 + 2.367 10−2. This equation places constraints on the calculation of keff, ρsnow and σ in avalanche warning models where σ is a key variable. For our samples, we calculate σ from measured values of keff and ρsnow using our equation and compare the value to that predicted by the French MEPRA avalanche warning model, which uses density and grain type as input data. MEPRA and the prediction of σ based on keff and ρsnow agree within 8%. MEPRA agrees with observations within 11%. Calculating σ from density only yields values 55% lower than measured, showing the interest of using additional data to predict σ.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".