Spatial variability and trends in observed snow depth 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
This study uses a gridded dataset of daily U.S. and Canadian surface observations from 1960–2000 to study regional spatial and temporal variability and trends in snow depth across North America. Analysis shows minimal change in North American snow depth through January, with regions of decreasing snow depths beginning in late January. These regional decreases grow in intensity and extent through March and into April, implying an earlier onset of spring melt. The region showing the greatest decreases in snow depth occurs in central Canada, along a line from the Yukon Territory in northwestern Canada to the Great Lakes region. The regional decreases in spring snow depth across central Canada are likely a result of more rapid melt of shallower winter snowpacks, evident through shallower snow cover (2–10 cm) during May and October and a decrease in extent of deeper snowpacks (>40cm) through March and April.
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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 | 0.001 |
| 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) | 0.001 | 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 it