Spatiotemporal distribution of seasonal snow density in the Northern Hemisphere based on in situ observation
Bibliographic record
Abstract
The snow density is a fundamental variable of the snow physical evolution processes, which can reflect the snowpack condition due to the thermal and gravitational compaction. Snow density is a bridge to transfer snow depth to snow water equivalent (SWE) for the snow water resources research. Therefore, it is important to understand the spatiotemporal distribution of snow density for the appropriate estimation of SWE. In this study, in situ snow densities from more than 6000 stations in the Northern Hemisphere were used to analyze the spatial and temporal variations in snow density. The results displayed that snow density varied spatially and temporally in the Northern Hemisphere, with range of below 0.1 to over 0.4 g/cm 3 . The average snow densities in the mountainous regions of western North America, southeastern Canada, and Europe range from approximately 0.24 to 0.26 g/cm 3 , which is significantly greater than the values of 0.16 to 0.17 g/cm 3 observed in Siberia, central Canada, the Great Plains of the United States, and China. The seasonal growth rates also present large spatial heterogeneity. The rates are over 0.024 g/cm 3 per month in Southeastern Canada, the west mountain of North America and Europe, approximately 0.017 g/cm 3 per month in Siberia, much larger than approximately 0.004 g/cm 3 per month in other regions. Snow cover duration is a critical factor to determine the snow density. This study endorses the small snow density in China based on meteorological station observations, which results from that the meteorological stations are dominantly distributed in plain areas with relative short snow cover duration and shallow snow.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".