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Record W2033897510 · doi:10.1088/2041-8205/749/2/l39

PRODUCTION OF NEAR-EARTH ASTEROIDS ON RETROGRADE ORBITS

2012· article· en· W2033897510 on OpenAlexaff
Sarah Greenstreet, Brett Gladman, Henry Ngo, Mikael Granvik, S. M. Larson

Bibliographic record

VenueThe Astrophysical Journal Letters · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsQueen's UniversityUniversity of British Columbia
Fundersnot available
KeywordsAsteroidMeteoroidNear-Earth objectPopulationCelestial mechanicsAsteroid beltPhysicsJupiter (rocket family)CometMean motionAstronomyGeologyOrbit (dynamics)AstrobiologyAstrophysicsPlanetSpace explorationAerospace engineering

Abstract

fetched live from OpenAlex

While computing an improved near-Earth object (NEO) steady-state orbital distribution model, we discovered in the numerical integrations the unexpected production of retrograde orbits for asteroids that had originally exited from the accepted main-belt source regions. Our model indicates that ∼0.1% (a factor of two uncertainty) of the steady-state NEO population (perihelion q < 1.3 AU) is on retrograde orbits. These rare outcomes typically happen when asteroid orbits flip to a retrograde configuration while in the 3:1 mean-motion resonance with Jupiter and then live for ∼0.001 to 100 Myr. The model predicts, given the estimated near-Earth asteroid (NEA) population, that a few retrograde 0.1–1 km NEAs should exist. Currently, there are two known MPC NEOs with asteroidal designations on retrograde orbits which we therefore claim could be escaped asteroids instead of devolatilized comets. This retrograde NEA population may also answer a long-standing question in the meteoritical literature regarding the origin of high-strength, high-velocity meteoroids on retrograde orbits.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

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 head0.010
GPT teacher head0.209
Teacher spread0.199 · 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 designSimulation or modeling
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

Citations34
Published2012
Admission routes1
Has abstractyes

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