Bibliographic record
Abstract
OBJECTIVES: In the days immediately following the terror attacks of 9/11, thousands of Americans chose to drive rather than to fly. We analyzed highway accident data to determine whether or not the number of fatalities and injuries following 9/11 differed from those in the same time period in 2000 and 2002. METHODS: Motor crash data from the National Highway Traffic Safety Administration's Fatality Analysis Reporting System were analyzed to determine the numbers and rates of fatalities and injuries nationally and in selected states for the 20 days after September 11, in each of 2000, 2001, and 2002. RESULTS: While the fatality rate did not change appreciably, the number of less severe injuries was statistically higher in 2001 than in 2000, both nationally and in New York State. CONCLUSIONS: The fear of terror attacks may have compelled Americans to drive instead of fly. They were thus exposed to the heightened risk of injury and death posed by driving. The need for public health to manage risk perception and communication is thus heightened in an era of global fear and terrorism.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".