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Record W4221048146 · doi:10.1029/2022ja030294

Resonance Maps for 3D Alfvén Waves in a Compressed Dipole Field

2022· article· en· W4221048146 on OpenAlexfundno aff
Andrew N. Wright, A. W. Degeling, Tom Elsden

Bibliographic record

VenueJournal of Geophysical Research Space Physics · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGeomagnetism and Paleomagnetism Studies
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaLeverhulme TrustScience and Technology Facilities CouncilUniversity of GlasgowUniversity of Alberta
KeywordsPhysicsResonance (particle physics)DipoleMagnetic fieldField (mathematics)Magnetic dipoleComputational physicsClassical mechanicsMathematicsAtomic physicsQuantum mechanicsPure mathematics

Abstract

fetched live from OpenAlex

Abstract Resonance Maps depict the possible locations and polarizations of resonant Alfvén waves (Field Line Resonances – FLRs) for a given equilibrium and driving frequency. Previously the use of Resonance Maps has been developed for gaining insight into the behavior of Alfvén waves in basic potential magnetic fields that allow the use of an orthogonal field aligned coordinate system. In more general magnetic fields these coordinates do not exist. In this paper we explore the application of Resonance Maps to such equilibria. A number of simulations of resonant Alfvén waves are presented and interpreted using the Maps. We find that Resonance Maps remain useful when some of the constructions used previously are generalised to accommodate the properties of more general magnetic fields. For example, Resonance Maps are able to predict the location and polarisation of Alfvén waves which are driven strongly by fast mode waves using a “tangential alignment condition”. Unusual properties, such as Alfvén waves crossing flux surfaces persist in the more general magnetic fields we consider.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.553
Threshold uncertainty score0.537

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.030
GPT teacher head0.326
Teacher spread0.297 · 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 designNot applicable
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

Citations12
Published2022
Admission routes1
Has abstractyes

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