Reflectance spectra of iron meteorites: Implications for spectral identification of their parent bodies
Bibliographic record
Abstract
Abstract– The 0.35–2.5 μm reflectance spectra of iron meteorite powders and slabs have been studied as a function of composition, surface texture (for slabs), grain size (for powders), and viewing geometry (for powders). Powder spectra are invariably red‐sloped over this wavelength interval and have a narrow range of visible albedos (approximately 10–15% at 0.56 μm). Metal (Fe:Ni) compositional variations have no systematic effect on the powder spectra, increasing grain size results in more red‐sloped spectra, and changes in viewing geometry have variable effects on overall reflectance and spectral slope. Roughened metal slab spectra have a wider, and higher, range of visible albedos than powders (22–74% at 0.56 μm), and are also red‐sloped. Smoother slabs exhibit greater differences from iron meteorite powder spectra, exhibiting wider variations in overall reflectance, spectral slopes, and spectral shapes. No unique spectral parameters exist that allow for powder and slab spectra to be fully separated in all cases. Spectral differences between slabs and powders can be used to constrain possible surface properties, and causes of rotational spectral variations, of M‐asteroids. The magnitude of spectral variations between M‐asteroids and rotational and spectral variability does not necessarily imply a dramatic change in surface properties, as the differences in albedo and/or spectral slope can be accommodated by modest changes in grain size (for powders), small changes in surface roughness (for slabs), or variations in viewing geometry. Since metal powders exhibit much less spectral variability than slabs, M‐asteroid spectral variability requires larger changes in either powder properties or viewing geometry than for slabs for a given degree of spectral variation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 |
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".