Large‐scale teleconnection patterns and synoptic climatology of major snow‐avalanche winters in the Presidential Range (New Hampshire, <scp>USA</scp>)
Bibliographic record
Abstract
ABSTRACT The relationships between the synoptic climatology, large‐scale teleconnections and the regional avalanche activity index (RAAI) inferred from tree‐rings were evaluated for the Presidential Range in the White Mountains (New Hampshire, USA). During the period 1936–2012, 18 years of regional avalanche activity were compared with the winter‐scale prevailing joint temperature/climatic modes (cold/wet (CW), cold/dry (CD), warm/wet (WW) and warm/dry (WW)), the North Atlantic Oscillation (NAO), the El Niño‐Southern Oscillation (ENSO), the 500‐mbar composite anomaly maps and the ratio of snow from different storm tracks. The total winter snowfall and the NAO negatively correlate with the RAAI. There is also a significant difference in avalanche activity between winters with a NAO under or above −3. Winters of regional avalanche activity were present in the four climatic modes, albeit the ratio of avalanche/non‐avalanche years is superior for CW winters compared to the three other modes, as well as for wet winters compared to dry winters. CW, CD and WW winters exhibit a negative NAO anomaly, which is eastbound for the wet years. Cold winters (CW, CD) receive more snow from the Great Lakes, whereas coastal depressions are more important during wet winters (CW, WW). The NAO is an adequate predictor of snowfall, but does not provide information about the storm tracks. On the contrary, the ENSO is poorly correlated with snowfall, but its relationship is significant with the ratio of snow produced by coastal depressions (positive relationship) and Great Lakes storms (negative relationship). These are the first results quantifying the atmospheric circulation – synoptic meteorology – snow avalanche relationships in Northeastern North America.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".