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Record W2532278570 · doi:10.3847/1538-4357/833/2/140

ASSESSING MAGNETIC TORQUES AND ENERGY FLUXES IN CLOSE-IN STAR–PLANET SYSTEMS

2016· article· en· W2532278570 on OpenAlexaff
Antoine Strugarek

Bibliographic record

VenueThe Astrophysical Journal · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPlanetPhysicsMagnetic energyStar (game theory)AstrophysicsKepler-69cMagnetic fieldPlanetary systemMagnetization

Abstract

fetched live from OpenAlex

ABSTRACT Planets in close-in orbit interact with the magnetized wind of their hosting star. This magnetic interaction was proposed to be a source for enhanced emissions in the chromosphere of the star, and to participate in setting the migration timescale of the close-in planet. The efficiency of the magnetic interaction is known to depend on the magnetic properties of the host star and of the planet, and on the magnetic topology of the interaction. We use a global, three-dimensional numerical model of close-in star–planet systems, based on the magnetohydrodynamics approximation, to compute a grid of simulations for varying properties of the orbiting planet. We propose a simple parametrization of the magnetic torque that applies to the planet, and of the energy flux generated by the interaction. The dependency upon the planet properties and the wind properties is clearly identified in the derived scaling laws, which can be used in secular evolution codes to take into account the effect of magnetic interactions in planet migration. They can also be used to estimate a potential magnetic source of enhanced emissions in observed close-in star–planet systems, in order to constrain observationally possible exoplanetary magnetic fields.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.640
Threshold uncertainty score0.262

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)0.0000.000

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 head0.008
GPT teacher head0.216
Teacher spread0.208 · 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

Citations58
Published2016
Admission routes1
Has abstractyes

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