The Risk of Groundling Fatalities from Unintentional Airplane Crashes
Bibliographic record
Abstract
The crashes of four hijacked commercial planes on September 11, 2001, and the repeated televised images of the consequent collapse of the World Trade Center and one side of the Pentagon will inevitably change people's perceptions of the mortality risks to people on the ground from crashing airplanes. Goldstein and colleagues were the first to quantify the risk for Americans of being killed on the ground from a crashing airplane for unintentional events, providing average point estimates of 6 in a hundred million for annual risk and 4.2 in a million for lifetime risk. They noted that the lifetime risk result exceeded the commonly used risk management threshold of 1 in a million, and suggested that the risk to "groundlings" could be a useful risk communication tool because (a) it is a man-made risk (b) arising from economic activities (c) from which the victims derive no benefit and (d) exposure to which the victims cannot control. Their results have been used in risk communication. This analysis provides updated estimates of groundling fatality risks from unintentional crashes using more recent data and a geographical information system approach to modeling the population around airports. The results suggest that the average annual risk is now 1.2 in a hundred million and the lifetime risk is now 9 in ten million (below the risk management threshold). Analysis of the variability and uncertainty of this estimate, however, suggests that the exposure to groundling fatality risk varies by about a factor of approximately 100 in the spatial dimension of distance to an airport, with the risk declining rapidly outside the first 2 miles around an airport. We believe that the risk to groundlings from crashing airplanes is more useful in the context of risk communication when information about variability and uncertainty in the risk estimates is characterized, but we suspect that recent events will alter its utility in risk communication.
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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 | 0.007 |
| 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 | 0.001 |
| Open science | 0.001 | 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".