Snow–precipitation coupling and related atmospheric feedbacks over North America
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
Understanding snow–precipitation coupling mechanisms is of great importance both from theoretical and operational considerations. Here, carefully designed climate model experiments, with and without interactive snow, are conducted to study snow–precipitation coupling mechanisms over North America. Coupling hotspots are identified over southern Canada during December and over the central United States during January. The hotspot over southern Canada involves a positive snow–atmosphere feedback mechanism, whereby snow modifies the large‐scale atmospheric features, which resembles the positive phase of North Atlantic Oscillation. This favors storm activity and enhanced snow over the region. The coupling over the central United States during January, on the other hand, is tied to the albedo effect of snow, which leads to cooling of the lower atmosphere, which in turn determines the precipitation phase, favoring snow formation over rain. The results from this study, in general, are informative for sub‐seasonal to seasonal prediction of winter precipitation for the studied regions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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