International Asteroid Warning Network Timing Campaign: 2019 XS
Bibliographic record
Abstract
Abstract As part of the International Asteroid Warning Network's observational exercises, we conducted a campaign to observe near-Earth asteroid 2019 XS around its close approach to Earth on 2021 November 9. The goal of the campaign was to characterize errors in the observation times reported to the Minor Planet Center, which become an increasingly important consideration as astrometric accuracy improves and more fast-moving asteroids are observed. As part of the exercise, a total of 957 astrometric observations of 2019 XS during the encounter were reported and subsequently were analyzed to obtain the corresponding residuals. While the timing errors are typically smaller than 1 s, the reported times appear to be negatively biased, i.e., they are generally earlier than they should be. We also compared the observer-provided position uncertainty with the cross-track residuals, which are independent of timing errors. A large fraction of the estimated uncertainties appear to be optimistic, especially when <0.″2. We compiled individual reports for each observer to help identify and remove the root cause of any possible timing error and improve the uncertainty quantification process. We suggest possible sources of timing errors and describe a simple procedure to derive reliable, conservative position uncertainties.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".