Measurement of specific surface area of fresh solid precipitation particles in heavy snowfall regions of Japan
Bibliographic record
Abstract
Abstract. In countries such as Japan, particular solid precipitation particles (PP) like unrimed PP and graupel sometimes form a weak layer in snow that subsequently triggers slab avalanches. It is therefore essential for avalanche prevention authorities to design a predictive model for slab avalanches triggered by weak PP layers. The specific surface area (SSA) is a parameter that could characterize the physical properties of PP. The SSAs of solid PP were measured for four winters (from 2013/2014–2016/2017) in Nagaoka, one of the heaviest snowfall-experiencing cities in Japan. More than 100 SSAs of PP were measured during the study period using the gas absorption method. The measured SSA values range from 42 to 153 m2 kg−1. PP under the melting condition show smaller values than that without melting. Unrimed and slightly rimed PP exhibited low SSA, whereas heavily rimed PP and graupel exhibited high SSA. The degree of riming depends on the synoptic meteorological conditions. Based on the potential of weak PP layer formation with respect to the degree of riming of PP, the results indicate that SSA is a useful parameter for describing the characteristics of PP to predict avalanches triggered by weak PP layers. The study found that the values of SSA strongly depend on wind speed (WS) and wet-bulb temperature (Tw) on the ground. SSA increases with increase in WS, and decreases with increase in Tw. Using WS and Tw, an equation to empirically estimate the SSA of fresh PP in Nagaoka was established. The equation successfully reproduced the fluctuation of SSA. The SSA equation, with the meteorological data, is an efficient first step toward describing the development of weak PP layers in the snow cover models.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".