Examining the Continuum of Injury Severity: Pooling the GES and FARS Datasets
Bibliographic record
Abstract
Given the import of the consequences of motor vehicle crashes, transportation safety researchers examined the influence of exogenous variables on vehicle occupant injury severity. Our study focuses on identifying the associated risk factors of driver fatalities while recognizing that fatality is not a single state but rather is made up of multiple discrete states from dying instantly to dying within the thirty days of crash by using the data from Fatality Analysis Reporting System (FARS). The research also simultaneously examines the whole spectrum of injury severity on an eleven point ordinal severity scale - no injury to fatality characterized as instant death by using a pooled dataset from FARS and General Estimates System (GES) dataset. The data pooling exercise is done to replace the “less informative” fatal crashes in the GES databases with the more detailed fatal crashes from FARS database. The data for the current study is sourced from the FARS and GES databases for the year 2010. The generalized ordered logit approach is considered for the empirical analyses. The important control variables that affect the both the early fatality risk and the injury severity outcome of the drivers include: driving under the influence of alcohol, medium or higher speed limits and increase in vehicle age.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".