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Record W4406656669 · doi:10.1063/5.0225399

The generation of whistler, lower hybrid, and magnetosonic waves by satellites passing through ionospheric magnetic field aligned irregularities

2025· article· en· W4406656669 on OpenAlexfundno aff
Bengt Eliasson, P. A. Bernhardt

Bibliographic record

VenuePhysics of Plasmas · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilOffice of the Director of National IntelligenceIntelligence Advanced Research Projects ActivityUniversity of Calgary
KeywordsPhysicsWhistlerIonosphereMagnetic fieldPlasmaComputational physicsField (mathematics)Quantum electrodynamicsGeophysicsNuclear physicsQuantum mechanics

Abstract

fetched live from OpenAlex

A numerical study is carried out for the generation of lower hybrid, whistler, and compressional Alfvén (magnetosonic) waves by satellites crossing magnetic field aligned irregularities or striations. Satellites and space debris propagating at an altitude of about 300 km with a velocity of 7.7 km/s perpendicular to the magnetic field generate a wake of lower hybrid waves with a wavelength of ∼1 m and frequencies near the lower hybrid frequency ∼7.87 kHz. In the presence of small-scale striations having widths below 0.5 m, the satellite-generated lower hybrid waves efficiently mode convert to whistler waves with frequencies slightly above the lower hybrid frequency, which propagate within a cone ∼19.5° to the background magnetic field. For larger striations having widths of 1 m and above, the interaction with satellites leads to modulated pulses of whistler waves as well as to magnetosonic waves propagating at large angles to the magnetic field, with frequencies below the lower hybrid frequency. The results are consistent with recent observations during conjunctures between satellites, where the observed frequencies ranged from the ion cyclotron frequency to the lower hybrid frequency [Bernhardt et al., Phys. Plasmas 30, 092106 (2023)].

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.507
Threshold uncertainty score0.655

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 head0.007
GPT teacher head0.217
Teacher spread0.210 · 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 designTheoretical or conceptual
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

Citations6
Published2025
Admission routes1
Has abstractyes

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