Characterizing and Predicting Traffic Accidents in Extreme Weather Environments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Motorists are vulnerable to extreme weather events, which are likely to be exacerbated by climate change throughout the world. Traffic accidents are conceptualized in this article as the result of a systemic failure that includes human, vehicular, and environmental factors. The snowstorm and concurrent accidents that occurred in the Northeastern United States on 26 January 2011 are used as a case study. Traffic accident data for Fairfax County, Virginia, are supplemented with Doppler radar and additional weather data to characterize the spatiotemporal patterns of the accidents resulting from this major snowstorm event. A kernel density smoothing method is implemented to identify and predict patterns of accident locations within this urban area over time. The predictive capability of this model increases over time with increasing accidents. Models such as these can be used by emergency responders to identify, plan for, and mitigate areas that are more susceptible to increased risk resulting from extreme weather events.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it