Uncontrolled reentries of space objects and aviation safety
Bibliographic record
Abstract
There is a growing risk to aircraft in flight from collisions with debris produced by the breakup of uncontrolled rocket bodies and satellites during atmospheric reentry. On one hand, the aviation industry has grown to almost 39 million flights per year, with a 63% increase between 2004 and 2019. On the other hand, the number of successful rocket launches more than doubled between 2015 and 2023, from 87 to 212, respectively. During those 212 launches in 2023 128 rocket bodies were abandoned in orbit, left to reenter uncontrollably. Such uncontrolled space object reentries are hard to predict, making mitigation measures difficult. In November 2022, a reentering Long March 5B rocket caused the closure of airspace over Europe, delaying 645 flights and having a plausible economic impact of millions of Euros. There are international and domestic laws that might enable the recovery of economic losses resulting from uncontrolled reentries, but such losses should not be allowed to occur in the first place. Instead of leaving the location of a reentry to chance, controlled reentries can be achieved with existing technologies and mission designs, directing reentries away from areas of high aircraft traffic. Moving to a controlled reentry regime would create a cost to space operators – but that cost is currently being externalized to the aviation industry. Multilateral solutions to create a controlled reentry regime should be pursued, as recommended in the 2023 Montreal Recommendations on Aviation Safety and Uncontrolled Space Object Reentries, before there is a tragedy caused by an aircraft collision with debris from a rocket body or satellite.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".