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Record W4415449445 · doi:10.1002/asna.70054

Spectral Diversity on (7) Iris and (10) Hygiea Revealed by Broadband Colors

2025· article· en· W4415449445 on OpenAlexfundno aff
Alberto Silva Betzler, O. F. de Sousa

Bibliographic record

VenueAstronomische Nachrichten · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsnot available
FundersScience and Technology Facilities CouncilQueen's UniversityQueen's University BelfastCalifornia Institute of TechnologyNational Aeronautics and Space AdministrationNuclear Safety and Security CommissionSpace Telescope Science InstituteNational Science Foundation
KeywordsAsteroidBroadbandSpectral signatureStellar classificationIRIS (biosensor)Spectral propertiesSpectral analysis

Abstract

fetched live from OpenAlex

ABSTRACT We present evidence of surface spectral diversity on asteroids (7) Iris and (10) Hygiea based on Johnson–Cousins (B–V, V–R) and ATLAS (c–o) color indices. For Iris, B–V shows significant rotational modulation, revealing localized heterogeneities associated with features such as the Xanthos and Porphyra craters. Its B–R spectral slope, per , matches that of Q/S‐type asteroids, while phase‐resolved variations suggest combined effects of composition, topography, and illumination geometry. In contrast, Hygiea is spectrally uniform: neither B–V nor V–R varies with rotation, and its B–R slope, per , is typical of the B/C‐complex. The c–o distributions for both objects do not show a clear dependence on Earth‐based viewing geometry. We estimate the c–o index of near‐Earth asteroid as 0.5, consistent with a redder K/L‐type surface. This result indicates that c–o can discriminate between S‐ and C‐complex asteroids, similar to V–R. These findings demonstrate the diagnostic value of broadband colors for characterizing asteroid surfaces and provide new insights into the compositional and evolutionary diversity of main‐belt asteroids.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.259
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.214
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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