Lagged relationships between North American snow mass and atmospheric teleconnection indices
Bibliographic record
Abstract
Abstract Relationships between North American winter (January, February, March or JFM) snow mass, or snow water equivalent (SWE), between 1980 and 1997, and four teleconnection indices are explored at different spatial and temporal scales, with teleconnection indices leading SWE by one to two seasons. Summer (July, August, September, or JAS) and fall (October, November, December, or OND) Pacific North American pattern (PNA), North Atlantic Oscillation (NAO), El‐Nino Southern Oscillation (ENSO), and the Pacific Decadal Oscillation (PDO) are included in this analysis. Principal components analysis of the SWE data set results in four components, explaining over 56% of the variance in the SWE signal which have significant relationships to ENSO, PDO, and NAO. Strong spatial components associated with these relationships emerge, with the first component (NAO, ENSO) located in the northcentral to northwestern regions of the United States and the southcentral to southwestern regions of Canada. A third component (PDO) stretches from the midwest to the east coast of the United States, New England, and the Atlantic Provinces. Ranked correlation analyses using the SWE data set, and additional analyses of station observations in order to extend the time domain, corroborate and elucidate the PCA results. Examinations of these relationships at different spatial scales, and over varying time domains, indicate that there may be some scale‐dependant predictive ability for North American snow mass. Copyright © 2006 Royal Meteorological Society.
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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) | 0.000 | 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".