Quality Assessment of Stereophotoclinometry as a Shape Modeling Method Using a Synthetic Asteroid
Bibliographic record
Abstract
Abstract The stereophotoclinometry (SPC) software suite has been used to generate global digital terrain models (DTMs) of many asteroids and moons, and was the primary tool used by the Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer (OSIRIS-REx) mission to model the shape of asteroid Bennu. We describe the dedicated preflight testing of SPC for the OSIRIS-REx mission using a synthetic “truth” asteroid model. SPC has metrics that determine the internal consistency of a DTM, but it was not known how these metrics are related to the absolute accuracy of a DTM, which was important for the operational needs of the mission. The absolute accuracy of an SPC-generated DTM cannot be determined without knowing the truth topography. Consequently, we developed a realistic, but synthetic, computer-generated representation of asteroid Bennu, photographed this synthetic truth model in an imaging campaign similar to that planned for the OSIRIS-REx mission, and then generated a global SPC DTM from these images. We compared the SPC DTM, which was represented by a radius every 70 cm across the asteroid surface, to the synthetic truth model to assess the absolute accuracy. We found that the internal consistency can be used to determine the 3D root-mean-square accuracy of the model to within a factor of two of the absolute accuracy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".