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Record W4411370386 · doi:10.1063/5.0245096

Magnetohydrodynamical thermoresistive instability and the Claws of chaos

2025· article· en· W4411370386 on OpenAlexafffund
Raphaël Hardy, Paul Charbonneau, A. Cumming

Bibliographic record

VenueChaos An Interdisciplinary Journal of Nonlinear Science · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsUniversité de MontréalMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInstabilityPhysicsChaoticAttractorAperiodic graphParameter spaceNonlinear systemClassical mechanicsMechanicsStatistical physicsMathematical analysisMathematicsQuantum mechanicsComputer science

Abstract

fetched live from OpenAlex

Exoplanets known as hot Jupiters offer a unique testbed for the study of the magnetohydrodynamical thermoresistive instability. This instability arises when ohmic heating enhances the electrical conductivity in a positive feedback loop leading to a thermal runaway. The heat equation, coupled with the momentum and magnetic induction equations form a strongly coupled non-linear third order system, from which chaotic behavior emerges naturally. We first illustrate and discuss the dynamical impact of thermoresistive instability in a representative solution in which the instability recurs in the form of periodic bursts. We then focus on the physical parameter regime in which aperiodic behavior occurs and demonstrate its chaotic nature. The chaotic regime turns out to be restricted to a relatively narrow region of parameter space within the domain where the thermoresistive instability occurs, on either side of which different classes of non-chaotic periodic behavior are observed. Through a linear stability analysis, we showcase how chaos appears at the transition between these dynamically distinct oscillatory regimes, which may be understood as overdamped and damped nonlinear oscillations.

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)0.001
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 score0.090
Threshold uncertainty score0.885

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.009
GPT teacher head0.294
Teacher spread0.286 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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