Orbit Estimation for Late Warning Asteroid Impacts: The Case of 2014 AA
Bibliographic record
Abstract
We describe a computational technique to assess the near-term Earth impact hazard posed by newly discovered asteroids. In these late warning cases the observational data sets will often include only an hour or so of tracking, leading to a severe degeneracy in the orbit estimation. The systematic ranging approach attacks this problem by exploring the poorly-constrained space of geocentric range and range rate, while the plane of sky position and motion is readily derived from the recorded observations. A raster scan in the two-dimensional range-range rate space allows us to identify regions corresponding to collision solutions, from which we derive rigorous impact probabilities, as well as potential impact times and locations. As an example, we shall consider the case of 2014 AA, a small asteroid that was discovered from Arizona by the Catalina Sky Survey early on January 1, 2014, and—as evidenced by infrasound monitoring—impacted the Atlantic Ocean less than a day later.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".