A spatio-temporal analysis of trends in Northern Hemisphere snow-dominated area and duration, 1971–2014
Bibliographic record
Abstract
ABSTRACT Seasonal snow-cover modulates water and energy budgets across large areas of the Northern Hemisphere. Previous research, based on satellite imagery interpreted and curated by the Rutgers University Snow Laboratory, has identified significant negative and positive trends in annual snow-covered duration and area at hemispheric and continental scales between 1971 and 2014. This study uses the same dataset to generate more detailed descriptions of spatial variations in these trends, maps intraannual variations in sign, statistical significance and strength, and quantifies associations with latitude and elevation. It also considers the limitations and uncertainties associated with a binary classification of this type, and the implications for trend magnitudes of adopting alternatives to the conventional assumption of 100% (0%) actual fractional snow-covered area in ‘snow-covered’ (‘snow-free’) spatial units at different stages of the snow-season. This prompts adoption of alternative terminology, referring to ‘snow-dominated’ area and duration. In response to questions about the dataset's veracity raised by some prior studies, it discusses climatological factors of potential relevance in explaining spatio-temporal trend patterns, and considers how biases might possibly have been introduced as a result of extraneous influences.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".