The Influence of Vapor Attachment Kinetics on Snow Effective Properties
Bibliographic record
Abstract
Proper modelling of heat and mass transfer in snow is a prerequisite for understanding snow metamorphism and simulating the mass and energy budget of a snowpack and the underlying ground. The transfer of heat and water vapor in snow can be described with macroscopic conservation equations, which include effective coefficients such as the snow thermal conductivity or the snow water vapor diffusion coefficient. Here, we investigate the impact of the surface kinetics of water vapor sublimation and deposition at the microscopic scale on these macroscopic equations, restraining ourselves to the limiting cases of slow and fast kinetics. In particular, we show that under the assumption of fast kinetics the thermal behavior of snow is similar to that of a regular inert medium, but with an enhanced conduction in the pores, due to latent heat transported with water vapor. Besides, faster kinetics increases the effective water vapor diffusion coefficient, which nonetheless remains less than that in free air. Most (but not all) available experimental investigations suggest that in snow, fast surface kinetics prevails, so that our results have numerous implications for the proper simulation of heat and mass transfer in snow.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".