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Record W4290072630 · doi:10.1038/s41467-022-32288-y

Inferring interiors and structural history of top-shaped asteroids from external properties of asteroid (101955) Bennu

2022· article· en· W4290072630 on OpenAlexaff
Yun Zhang, Patrick Michel, O. S. Barnouin, J. H. Roberts, M. G. Daly, Ronald‐Louis Ballouz, K. J. Walsh, D. C. Richardson, Christine Hartzell, D. S. Lauretta

Bibliographic record

VenueNature Communications · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsYork University
FundersCentre National d’Etudes SpatialesEuropean CommissionSolar System Exploration Research Virtual InstituteNational Aeronautics and Space Administration
KeywordsAsteroidRubbleCohesion (chemistry)AstrobiologyGeologyPhysicsGeotechnical engineering

Abstract

fetched live from OpenAlex

Abstract Asteroid interiors play a key role in our understanding of asteroid formation and evolution. As no direct interior probing has been done yet, characterisation of asteroids’ interiors relies on interpretations of external properties. Here we show, by numerical simulations, that the top-shaped rubble-pile asteroid (101955) Bennu’s geophysical response to spinup is highly sensitive to its material strength. This allows us to infer Bennu’s interior properties and provide general implications for top-shaped rubble piles’ structural evolution. We find that low-cohesion (≲0.78 Pa at surface and ≲1.3 Pa inside) and low-friction (friction angle ≲ 35 ∘ ) structures with several high-cohesion internal zones can consistently account for all the known geophysical characteristics of Bennu and explain the absence of moons. Furthermore, we reveal the underlying mechanisms that lead to different failure behaviours and identify the reconfiguration pathways of top-shaped asteroids as functions of their structural properties that either facilitate or prevent the formation of moons.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.018
GPT teacher head0.231
Teacher spread0.213 · 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

Citations49
Published2022
Admission routes1
Has abstractyes

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