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Record W2331169979 · doi:10.1017/s1743921313012647

On the Planetary Orbital Period Ratio Distribution In Multiple Planet Systems

2012· article· en· W2331169979 on OpenAlexaff
Ji‐Wei Xie

Bibliographic record

VenueProceedings of the International Astronomical Union · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPlanetMean motionAsymmetryPhysicsOrbital periodSolar SystemResonance (particle physics)Distribution (mathematics)Transit (satellite)Period (music)AstronomyAstrophysicsMathematicsStarsMathematical analysisEngineeringAcoustics

Abstract

fetched live from OpenAlex

Abstract Many multiple planet systems have been found by both radial velocity (RV) and transit surveys, such as theKeplermission. Period ratio distribution of these planet candidates show that they do not prefer to be in or near Mean Motion Resonance (MMR). Nevertheless, there are small but significant excesses of candidate pairs both spaced slightly exterior to exact resonance, particular near the first order of MMR, such as 2:1 and 3:2. Here, we first review recent observational constraints on these multiple transiting systems and theoretical models, which attempt to understand their period ratio distributions. Then we identify a statistical effect based on an intrinsic asymmetry associated with MMR, and find it play an important role in shaping the period ratio distribution near MMR. Last but least, we also find such an intrinsic asymmetry is existing in asteroids of our solar system.

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)0.003
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.010
GPT teacher head0.187
Teacher spread0.178 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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