Coupled human-natural system impacts of a winter weather whiplash event
Bibliographic record
Abstract
In October 2011, the Halloween Nor’easter produced unusually early and heavy snowfall while leaves were still on the trees, causing extensive damage throughout the northeastern United States. This storm is an example of winter weather whiplash, in which an abrupt, back-and-forth swing in winter weather affects coupled human and natural systems. Research on the social-ecological drivers and impacts of winter weather whiplash is scarce because most studies only consider meteorological causes and consequences of extreme events. In this study, we used publicly available data of snowfall accumulation, vegetation phenology, road density, and per capita income to predict storm impacts, which we estimated with textual analysis of Halloween Nor’easter newspaper coverage. We demonstrated that a combination of meteorological, natural, and human system drivers was better able to predict the impact of the storm than meteorological drivers alone. Although we focused on the Halloween Nor’easter, our work highlights the necessity of understanding how multiple drivers and hazards can intersect to create rare and possibly novel conditions that may become more common as the climate warms and becomes more variable.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".