MétaCan
Menu
Back to cohort
Record W3014828718 · doi:10.1029/2019ja027571

Modeling Non‐Force‐Free and Deformed Flux Ropes in Titan's Ionosphere

2020· article· en· W3014828718 on OpenAlexaff
C. J. Martin, C. S. Arridge, S. V. Badman, Daniel Billett, Clarissa J. Barratt

Bibliographic record

VenueJournal of Geophysical Research Space Physics · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsUniversity of Saskatchewan
FundersScience and Technology Facilities Council
KeywordsRopePhysicsTitan (rocket family)Magnetic fieldFlux (metallurgy)Magnetic fluxMechanicsStructural engineeringMaterials scienceEngineering

Abstract

fetched live from OpenAlex

Abstract Previous work at Titan presented a set of 85 flux ropes detected during Cassini flybys of Titan from 2005 to 2017. In that study a force‐free model was used to determine the radii and axial magnetic field of the flux ropes. In this work we apply non‐force‐free models. The non‐force‐free model shows an improvement in the number of flux ropes that can be fitted with a model, along with improved uncertainties and χ 2 values. A number of asymmetries and features in the magnetometer data cannot be reproduced by either model; therefore, we deform the force‐free model to show that small deformations can replicate these features. One such deformation is to use an elliptical cross section, which replicates a plateau in magnetic field strength along with asymmetries on either side of the center of the flux ropes. Additionally, we explore the properties of bending a flux rope, where we find that minimum variance analysis becomes increasingly degenerate with bending, along with a slight bend causing the switching of the axial field direction from intermediate to maximum variance direction. We conclude that the flux ropes at Titan show aspects of developing flux ropes, compared to other planetary bodies, which exhibit more agreement to the force‐free assumptions of mature flux ropes.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.580
Threshold uncertainty score0.505

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.042
GPT teacher head0.304
Teacher spread0.262 · 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 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

Citations3
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Geophysical Research Space PhysicsSame topicAstro and Planetary ScienceFrench-language works237,207