Snow Studies. Part I: A Study of Natural Variability of Snow Terminal Velocity
Bibliographic record
Abstract
Abstract The variability and the uncertainties in snowfall velocity measurements are addressed in this study. The authors consider (i) the instrumental uncertainty in the fall velocity measurement, (ii) the effect of unstable falling motion on the accuracy of velocity measurement, and (iii) the natural variability of homogeneous snow terminal fall velocity. It is shown that, when periods of homogeneous characteristics of snow are selected to minimize the mixture of particles of different origin, the standard deviation of snowfall velocity within each period tends to stabilize at a value between 0.1 and 0.2 m s−1. In addition, the variability of snow terminal fall velocity is examined with three control variables: surface temperature Ts, echo-top temperature Tt, and the depth of precipitation system H. The results show that the exponent b in the power-law relationship V = aDb has little effect on the variability of snowfall velocity: the coefficient a correlates much better with the control variables (Ts, Tt, H) than the exponent b. Hence, snowfall velocity can be modeled with a varying coefficient a and a fixed exponent b = 0.18 (V = aD0.18) with good accuracy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".