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Record W4400034266 · doi:10.1111/maps.14227

Asteroid (101955) Bennu in the laboratory: Properties of the sample collected by <scp>OSIRIS</scp>‐<scp>REx</scp>

2024· article· en· W4400034266 on OpenAlexfundno aff
D. S. Lauretta, H. C. Connolly, Joseph E. Aebersold, C. M. O'd. Alexander, Ronald‐L. Ballouz, Jessica Barnes, H. C. Bates, C. A. Bennett, Laurinne Blanche, E. H. Blumenfeld, S. J. Clemett, George D. Cody, D. N. DellaGiustina, Jason P. Dworkin, S. A. Eckley, Dionysis I. Foustoukos, I. A. Franchi, D. P. Glavin, R. C. Greenwood, Pierre Haenecour, V. E. Hamilton, D. H. Hill, T. Hiroi, K. Ishimaru, Fred Jourdan, H. H. Kaplan, L. P. Keller, A. J. King, Piers Koefoed, Melissa K. Kontogiannis, L. Le, R. J. Macke, T. J. McCoy, R. E. Milliken, Jens Najorka, A. N. Nguyen, M. Pajola, Anjani T. Polit, K. Righter, H. L. Roper, S. S. Russell, A. J. Ryan, Scott A. Sandford, P. F. Schofield, Cody Schultz, Laura Seifert, Shogo Tachibana, K. L. Thomas-Keprta, M. S. Thompson, Valerie Tu, Filippo Tusberti, Kun Wang, T. J. Zega, C. W. V. Wolner

Bibliographic record

VenueMeteoritics and Planetary Science · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsnot available
FundersLawrence Livermore National LaboratoryScience and Technology Facilities CouncilNational Air and Space MuseumJet Propulsion LaboratoryJapan Aerospace Exploration AgencyHokkaido UniversityInstitute of Space and Astronautical ScienceAgenzia Spaziale ItalianaYork UniversityJohnson Space CenterLawrence Berkeley National LaboratoryUniversiteit GentUniversity of OxfordNational Aeronautics and Space AdministrationPurdue UniversitySouthwest Research InstituteUniversity of Maryland, Baltimore CountyUniversity of Hawai'iNational Museum of Natural HistoryJapan Society for the Promotion of ScienceWashington University in St. LouisUniversity of OttawaCurtin University of TechnologyAmes Research CenterDeutsches Zentrum für Luft- und RaumfahrtJohns Hopkins UniversityGoddard Space Flight CenterNorthern Arizona UniversityCalifornia Institute of TechnologySmithsonian Institution
KeywordsPresolar grainsMeteoriteRegolithChondriteAstrobiologyMineralogyAsteroidGeologyGeochemistryCarbonaceous chondriteParent bodyChemistryPhysics

Abstract

fetched live from OpenAlex

Abstract On September 24, 2023, NASA's OSIRIS‐REx mission dropped a capsule to Earth containing ~120 g of pristine carbonaceous regolith from Bennu. We describe the delivery and initial allocation of this asteroid sample and introduce its bulk physical, chemical, and mineralogical properties from early analyses. The regolith is very dark overall, with higher‐reflectance inclusions and particles interspersed. Particle sizes range from submicron dust to a stone ~3.5 cm long. Millimeter‐scale and larger stones typically have hummocky or angular morphologies. Some stones appear mottled by brighter material that occurs as veins and crusts. Hummocky stones have the lowest densities and mottled stones have the highest. Remote sensing of Bennu's surface detected hydrated phyllosilicates, magnetite, organic compounds, carbonates, and scarce anhydrous silicates, all of which the sample confirms. We also find sulfides, presolar grains, and, less expectedly, Mg,Na‐rich phosphates, as well as other trace phases. The sample's composition and mineralogy indicate substantial aqueous alteration and resemble those of Ryugu and the most chemically primitive, low‐petrologic‐type carbonaceous chondrites. Nevertheless, we find distinct hydrogen, nitrogen, and oxygen isotopic compositions, and some of the material we analyzed is enriched in fluid‐mobile elements. Our findings underscore the value of sample return—especially for low‐density material that may not readily survive atmospheric entry—and lay the groundwork for more comprehensive analyses.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.009
GPT teacher head0.192
Teacher spread0.183 · 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 source (direct Gemma or distilled Codex), 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

Citations188
Published2024
Admission routes1
Has abstractyes

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