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Record W3018864045 · doi:10.1093/mnras/staa1069

Phase structure of co-orbital motion with Jupiter

2020· article· en· W3018864045 on OpenAlexafffund
Yi Qi, Anton de Ruiter

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsToronto Metropolitan University
FundersCanada Research Chairs
KeywordsPhysicsOrbital motionHamiltonian (control theory)Orbital inclinationTorusCelestial mechanicsPhase spaceOrbital elementsClassical mechanicsOrbital mechanicsMean motionAmplitudePlanetAstrophysicsQuantum mechanicsGeometryAstronomySatelliteAngular momentum

Abstract

fetched live from OpenAlex

ABSTRACT In this paper, we investigate the dynamics of the inclined co-orbital motion with Jupiter through a torus phase structure in the Sun–Jupiter circular restricted three-body problem. A semi-analytical method to establish the Hamiltonian approximation for the inclined co-orbital motion is proposed. Phase structures of different kinds of co-orbital behaviours are shown in the torus space clearly. Based on numerical computation, we analyse the evolution and the connection of different co-orbital dynamics. Summarizing results and conclusions in this paper, we find two main principles throughout the investigation of the co-orbital motion: (i) the libration amplitude of the resonant angle for the co-orbital motion is bounded by the corresponding Hamiltonian isosurface in the torus space and (ii) the co-orbital behaviour is influenced by collision curves, and with the decrease of the Hamiltonian value, the influence is more significant.

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 score0.003
Threshold uncertainty score0.009

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 studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.202
Teacher spread0.195 · 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

Citations14
Published2020
Admission routes2
Has abstractyes

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