Bibliographic record
Abstract
Abstract We describe results of a planetary defense exercise conducted during the close approach to Earth by the near-Earth asteroid (99942) Apophis during 2020 December–2021 March. The planetary defense community has been conducting observational campaigns since 2017 to test the operational readiness of the global planetary defense capabilities. These community-led global exercises were carried out with the support of NASA's Planetary Defense Coordination Office and the International Asteroid Warning Network. The Apophis campaign is the third in our series of planetary defense exercises. The goal of this campaign was to recover, track, and characterize Apophis as a potential impactor to exercise the planetary defense system including observations, hypothetical risk assessment and risk prediction, and hazard communication. Based on the campaign results, we present lessons learned about our ability to observe and model a potential impactor. Data products derived from astrometric observations were available for inclusion in our risk assessment model almost immediately, allowing real-time updates to the impact probability calculation and possible impact locations. An early NEOWISE diameter measurement provided a significant improvement in the uncertainty on the range of hypothetical impact outcomes. The availability of different characterization methods such as photometry, spectroscopy, and radar provided robustness to our ability to assess the potential impact risk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".