Temporal and spatial variability of North American prairie snow cover (1988–1995) inferred from passive microwave‐ derived snow water equivalent imagery
Bibliographic record
Abstract
Estimates of regional snow water equivalent (SWE) are essential for hydrological prediction, climatological analysis, and meteorological forecasting. Passive microwave‐derived estimates of snow cover have unique benefits such as all‐weather imaging, rapid scene revisit capabilities, and the ability to provide these quantitative SWE data. For this study the available time series of special sensor microwave/imager (SSM/)) brightness temperatures in the equal area SSM/I Earth grid projection were processed with the Canadian Atmospheric Environment Service dual‐channel SWE algorithm for a ground‐validated North American prairie region. Seven winter seasons (December, January, and February) of SWE imagery spanning 1988–1995 and averaged for 5 day intervals were subjected to a rotated principal components analysis (PCA) performed individually for each season. A final PCA considering all 7 winter seasons was performed in order to investigate the degree to which snow cover patterns reappear from one season to the next. Results indicate that modes of snow cover in the North American prairies are most persistent during the late winter (February) and exhibit a greater degree of variability during December than the other winter months. Two snow cover regimes are identified for the study region, with the winters of 1988/1989–1991/1992 characterized in a manner that is unique in both temporal and spatial aspects from the winters of 1992/1993–1994/1995.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".