The frequency of window damage caused by bolide airbursts: A quarter century case study
Bibliographic record
Abstract
Abstract We have empirically estimated how often fireball shocks produce overpressure (∆P) at the ground sufficient to damage windows. Our study used a numerical entry model to estimate the energy deposition and shock production for a suite of 23 energetic fireballs reported by U.S. Government sensors over the last quarter century. For each of these events, we estimated the peak ∆Pon the ground and the ground area above ∆Pthresholds of 200 and 500 Pa where light and heavy window damage, respectively, are expected. Our results suggest that at the highest ∆P, it is the rare, large fireballs (such as the Chelyabinsk fireball) which dominate the long‐term areal ground footprints for heavy window damage. The height at the fireball peak brightness and the fireball entry angle contribute to the variance in ground ∆P, with lower heights and shallower angles producing larger ground footprints and more potential damage. The effective threshold energy for fireballs to produce heavy window damage is ~5–10 kT; such fireballs occur globally once every 1–2 years. These largest annual bolide events, should they occur over a major urban center with large numbers of windows, can be expected to produce economically significant window damage. However, the mean frequency of heavy window damage (∆Pabove 500 Pa) from fireball shock waves occurring over urban areas is estimated to be approximately once every 5000 yr. Light window damage (∆Pabove 200 Pa) is expected every ~600 yr.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".