Building a High-resolution Digital Terrain Model of Bennu from Laser Altimetry Data
Bibliographic record
Abstract
Abstract The Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer (OSIRIS-REx) spacecraft orbited the near-Earth asteroid (101955) Bennu to characterize the asteroid prior to sampling. One important aspect of this characterization was the creation of a high-resolution (5–7 cm) global shape model using the OSIRIS-REx Laser Altimeter (OLA). We describe the data collected by OLA, along with the approach used to register overlapping topography using keypoints and keypoint descriptors in order to produce a globally self-consistent set of data. These globally registered sets of topographic scans were used to generate digital terrain models at both global and regional scales. We also describe efforts to correct for a change in behavior of the scanning mirror after the launch and highlight the improvements to the data after implementing an updated calibration of the mirror. The resulting model represents the highest-fidelity global OLA data set.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".