Bibliographic record
Abstract
Rain-on-snow ablation events carry a relatively high risk for rapid snowmelt and runoff due to the combination of liquid precipitation and generally high turbulent fluxes into the snowpack. Determining the variability in rain-on-snow ablation is critical in describing local hydroclimate. This study uses a gridded observational snow dataset to examine spatiotemporal variations in North American rain-on-snow ablation over a 50 yr period. Here we show rain-on-snow ablation represents approximately 33% of all ablation events in the eastern third of the continent, compared to <20% in its interior. Rain-on-snow ablation was most frequent along the western and eastern coasts of the continent, with >10 events observed per year on average. A central band of enhanced event frequencies propagated meridionally during the calendar year, most prominently in the eastern half of the continent. Seasonal (September to August) event frequency from 1960-2009 significantly decreased by approximately 50% across much of northern Quebec and in the southern Appalachians, while it significantly increased in portions of British Columbia and southeastern Quebec. Interannual variations in event frequency were primarily forced by variations in seasonal-scale snowfall and snow depth, and only moderately associated with variations in air temperatures.
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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) | 0.002 | 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".