Basalt or Not? Near-infrared Spectra, Surface Mineralogical Estimates, and Meteorite Analogs for 33 V<sub>p</sub>-type Asteroids
Bibliographic record
Abstract
Abstract Investigations of the main asteroid belt and efforts to constrain that population’s physical characteristics involve the daunting task of studying hundreds of thousands of small bodies. Taxonomic systems are routinely employed to study the large-scale nature of the asteroid belt because they utilize common observational parameters, but asteroid taxonomies only define broadly observable properties and are not compositionally diagnostic. This work builds upon the results of work by Hardersen et al., which has the goal of constraining the abundance and distribution of basaltic asteroids throughout the main asteroid belt. We report on the near-infrared (NIR: 0.7 to 2.5 μm) reflectance spectra, surface mineralogical characterizations, analysis of spectral band parameters, and meteorite analogs for 33 Vp asteroids. NIR reflectance spectroscopy is an effective remote sensing technique to detect most pyroxene group minerals, which are spectrally distinct with two very broad spectral absorptions at ∼0.9 and ∼1.9 μm. Combined with the results from Hardersen et al., we identify basaltic asteroids for ∼95% (39/41) of our inner-belt Vp sample, but only ∼25% (2/8) of the outer-belt Vp sample. Inner-belt basaltic asteroids are most likely associated with (4) Vesta and represent impact fragments ejected from previous collisions. Outer-belt Vp asteroids exhibit disparate spectral, mineralogical, and meteorite analog characteristics and likely originate from diverse parent bodies. The discovery of two additional likely basaltic asteroids provides additional evidence for an outer-belt basaltic asteroid population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".