Airspace closures due to reentering space objects
Bibliographic record
Abstract
Uncontrolled reentries of space objects create a collision risk with aircraft in flight. While the probability of a strike is low, the consequences could be catastrophic. Moreover, the risk is rising due to increases in both reentries and flights. In response, national authorities may choose to preemptively close airspace during reentry events; some have already done so. We determine the probability for a rocket body reentry within airspace over a range of air traffic densities. The highest-density regions, around major airports, have a 0.8% chance per year of being affected by an uncontrolled reentry. This rate rises to 26% for larger but still busy areas of airspace, such as that found in the northeastern United States, northern Europe, or around major cities in the Asia-Pacific region. For a given reentry, the collision risk in the underlying airspace increases with the air traffic density. However, the economic consequences of flight delays also increase should that airspace be closed. This situation puts national authorities in a dilemma-to close airspace or not-with safety and economic implications either way. The collision risk could be mitigated if controlled reentries into the ocean were required for all missions. However, over 2300 rocket bodies are already in orbit and will eventually reenter in an uncontrolled manner. Airspace authorities will face the challenge of uncontrolled reentries for decades to come.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".