Bibliographic record
Abstract
Snow accumulation and melt patterns play a significant role in the water, energy, carbon, and nutrient cycles in the montane environments of the Western United States. Recent studies have illustrated that changes in the snow/rainfall apportionments and snow accumulation and melt patterns may occur as a consequence of changes in climate in the region. In order to understand how these changes may affect the snow regimes of the region, the current characteristics of the snow accumulation and melt patterns must be identified. Here we characterize the snow water equivalent (SWE) curve formed by the daily SWE values at 766 snow pillow stations in the Western United States, focusing on several metrics of the yearly SWE curves and the relationships between the different metrics. The metrics are the initial snow accumulation and snow disappearance dates, the peak snow accumulation and date of peak, the length of the snow accumulation season, the length of the snowmelt season, and the snow accumulation and snowmelt slopes. Three snow regimes emerge from these results: a maritime, an intermountain, and a continental regime. The maritime regime is characterized by higher maximum snow accumulations reaching 300 cm and shorter accumulation periods of less than 220 days. Conversely, the continental regime is characterized by lower maximum accumulations below 200 cm and longer accumulation periods reaching over 260 days. The intermountain regime lies in between. The regions that show the characteristics of the maritime regime include the Cascade Mountains, the Klamath Mountains, and the Sierra Nevada Mountains. The intermountain regime includes the Eastern Cascades slopes and foothills, the Blue Mountains, Northern and Central basins and ranges, the Columbia Mountains/Northern Rockies, the Idaho Batholith, and the Canadian Rockies. Lastly, the continental regime includes the Middle and Southern Rockies, and the Wasatch and Uinta Mountains. The implications of snow regime classification are discussed in the context of possible changes in accumulation and melt patterns associated with regional warming.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".