Downrange Overflight Risk Analysis Methods for Space Shuttle Launches
Bibliographic record
Abstract
The Space Shuttle poses debris impact hazards to people and property during ascent, both during the initial liftoff phase of flight (Stage 1) and during the downrange overflight phase (Stage 2) where its projected debris impact footprint crosses over land areas. The assessment of the risks to the launch area from the initial liftoff debris impact hazard has been done routinely prior to Shuttle launches. However, the risks due to downrange overflight are only evaluated periodically to verify that the risk levels remain within acceptable limits. Recently a reassessment of the risks for Shuttle downrange overflight became a requirement for the returnto-flight of the Shuttle following the Columbia accident. The focus of this paper is on the methods and data used for the recalculation of the risks for downrange overflight, where the Shuttle poses hazards to Newfoundland and to areas of Europe, Asia and Africa. The Shuttle presents some interesting challenges for modeling the downrange overflight risks because of it’s various abort modes and the way the vehicle responds to various modes of failure. In order to improve the modeling of the overflight risks, NASA has generated new data; in particular new malfunction trajectories, guidance and performance dispersed trajectories, failure mode definitions, failure rates, and reentry breakup debris models. Based on this new data, new methods and algorithms to perform the downrange overflight risk calculations have been developed. This paper discusses the new Shuttle data and the unique methods used to develop the debris impact distributions used to evaluate the debris impact risks. The data are based on representative Shuttle trajectories to the International Space Station. Since the focus of the paper is on the data and methods, the resulting risks are only overviewed qualitatively.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".