Color Effects of Textural Variations on Bennu: Comparison of Analog Laboratory Mixtures with OSIRIS-REx Data
Bibliographic record
Abstract
OSIRIS-REx is a sample return mission to near-Earth Asteroid (101955) Bennu (Lauretta et al. 2017). The asteroid is spectrally classified as a B-type (Clark et al. 2011), and phyllosilicates similar to those found in carbonaceous chondrites have been detected on its surface (Hamilton et al. 2019). Bennu has a relatively flat (and blue) reflectance spectrum in the 0.4 to 3.7 micron spectral range and has a low albedo of ~4.5% (Golish et al. 2020).Bennu has a rough and rocky surface. Imaging data from the OSIRIS-REx Camera Suite (OCAMS; Rizk et al. 2018) reveals that boulders in the size range from 1 to 10 meters dominate the surface (Lauretta et al. 2019; DellaGiustina and Emery et al. 2019). Observed boulder textures range from smooth to hummocky and breccia-like (Walsh et al. 2019; DellaGiustina and Emery et al. 2019). The smoother rocks appear to be smaller, brighter, and more angular, while the rougher rocks appear to be larger, darker, and highly textured. Because spectral variations on Bennu are subtle and associated with albedo (Clark et al. 2019), the question arises: Could the observed color variations be due to texture variations alone, or are space-weathering variations required to explain the observations? To isolate the spectral effects of texture on the spectral properties of Bennu, we first simulate Bennu’s spectrum using a two-component mixture, then we check to see whether texture changes in this analog can account for the observed color and albedo trends. Simulated Bennu Spectral Analog: We synthesized physical mixtures of saponite (SAP105 with ~25 wt.% dolomite) with two forms of carbon:
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 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".