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Record W2110226683 · doi:10.1088/0004-637x/744/2/139

COLLISIONAL EVOLUTION OF ULTRA-WIDE TRANS-NEPTUNIAN BINARIES

2011· article· en· W2110226683 on OpenAlexaff
A. H. Parker, J. J. Kavelaars

Bibliographic record

VenueThe Astrophysical Journal · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsHerzberg Institute of AstrophysicsUniversity of Victoria
FundersNational Science Foundation
KeywordsPhysicsEccentricity (behavior)Solar SystemPopulationBinary numberTrans-Neptunian objectAstrophysicsOrbit (dynamics)Mass ratioAstronomyAerospace engineering

Abstract

fetched live from OpenAlex

The widely-separated, near-equal mass binaries hosted by the cold Classical Kuiper Belt are delicately bound and subject to disruption by many perturbing processes. We use analytical arguments and numerical simulations to determine their collisional life-times given various impactor size distributions, and include the effects of mass-loss and multiple impacts over the lifetime of each system. These collisional lifetimes constrain the population of small (R & 1 km) objects currently residing in the Kuiper Belt, and confirm that the size distribution slope at small size cannot be excessively steep — likely q. 3.5. We track mutual semi-major axis, inclination, and eccentricity evolution through our simulations, and show that it is unlikely that the wide binary population represents an evolved tail of the primordially-tight binary population. We find that if the wide binaries are a collisionally-eroded population, their primordial mutual orbit planes must have preferred to lie in the plane of the solar system. Finally, we find that current limits on the size distribution at small radii remain high enough that the prospect of detecting dust-producing collisions in real-time in the Kuiper Belt with future optical surveys is feasible. Subject headings: Kuiper belt: general — planets and satellites: dynamical evolution and stability 1.

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 score0.553
Threshold uncertainty score0.840

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)0.0010.000

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 head0.012
GPT teacher head0.195
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 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

Citations46
Published2011
Admission routes1
Has abstractyes

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