Climate Change Modeling and the Weather-Related Road Accidents (Poster)
Bibliographic record
Abstract
This presentation projects the climate change and studies the impact of climate change on hazardous weather-related road accidents in New Brunswick province of Canada. Climate change modeling uses 30-years daily weather records for the seven climate zones of New Brunswick, National Centers for Environmental Prediction (NCEP) re-analysis dataset, large-scale simulation data from the third generation Coupled Global Climate Model (CGCM3) and third version of Hadley Centre Coupled Model (HadCM3). Large-scale simulation data from Canadian GCM under SRES-A2, and SRES-A1B scenarios along with large-scale simulation data from Hadley center CM under SRES-A2, and SRES-B2 during the 21st century are used to model the climate change. The climate change modeling estimates the increasing rainy days for all climate zones; however, the number of snowy and freezing days may decrease or stay the same for most of the climate zones during three different future periods in 21st century (i.e. 2011-2040, 2041-2070, 2071-2100). This study also estimates an Exposure to Weather-Accident Severity (EWAS) index using both single and multiple road accident data. The negative binomial regression and Poisson regression models are applied to estimate the relationship between the EWAS index and weather-related explanatory variables of road accidents. Surface-weather condition, weather-driver’s gender, weather-driver’s age, weather-driver’s experience and weather-vehicle’s age have strong positive correlations with EWAS index. Surface-road alignment and surface-road characteristics have negative relationship with EWAS index. These relationships are similar at different census divisions of the New Brunswick. Increasing number of hazardous weather days estimated by the climate change modeling, and positive relationships among EWAS index and weather-related explanatory variables of road accidents suggest more hazardous weather-related accidents in future.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".