Estimating Ground Snow Load Based on Ground Snow Depth and Climatological Elements for Snow Hazard Assessment in Northeastern China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Extreme snow loads can collapse roofs. This load is calculated based on the ground snow load (that is, the snow water equivalent on the ground). However, snow water equivalent (SWE) measurements are unavailable for most sites, while the ground snow depth is frequently measured and recorded. A new simple practical algorithm was proposed in this study to evaluate the SWE by utilizing ground snow depth, precipitation data, wind speed, and air temperature. For the evaluation, the precipitation was classified as snowfall or rainfall according to the air temperature, the snowfall or rainfall was then corrected for measurement error that is mainly caused by wind-induced undercatch, and the effect of snow water loss was considered. The developed algorithm was applied and validated using data from 57 meteorological stations located in the northeastern region of China. The annual maximum SWE obtained based on the proposed algorithm was compared with that obtained from the actual SWE measurements. The return period values of the annual maximum ground snow load were estimated and compared to those obtained according to the procedure suggested by the Chinese structural design code. The comparison indicated that the use of the proposed algorithm leads to a good estimated SWE or ground snow load. Its use allowed the estimation of the ground snow load for sites without SWE measurement and facilitated snow hazard mapping.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it