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Record W2210889614 · doi:10.1002/2015ja021715

Using orbital tethers to remediate geomagnetic radiation belts

2016· article· en· W2210889614 on OpenAlexafffund
Mathias Hudoba de Badyn, R. Marchand, R. D. Sydora

Bibliographic record

VenueJournal of Geophysical Research Space Physics · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSolar and Space Plasma Dynamics
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPitch angleVan Allen radiation beltPhysicsRadiationComputational physicsScatteringParticle (ecology)Particle radiationCharged particleOrbit (dynamics)OpticsMagnetosphereMagnetic fieldAerospace engineeringAstronomyGeologyIon

Abstract

fetched live from OpenAlex

Abstract The Van Allen radiation belts pose a hazard to spacecraft and astronauts, and similar radiation belts around other planets pose a hazard to interplanetary probes. We discuss a method of remediating these radiation belts first proposed by Danilov and Vasilyev, and recently improved by Hoyt, Minor, and Cash, where a long, charged tether is placed in orbit inside a radiation belt. In this approach, an electric field of the tether scatters the belt particles into a pitch angle loss cone leading to absorption of the particles in the atmosphere. A test particle calculation is presented which computes the scattered pitch angle of belt particles as a function of initial pitch angle and gyrophase for different particle energies. The moments of the resulting distribution of scattered angle versus initial pitch angle are used to compute the number density of the belt as a function of time using a Fokker‐Planck diffusion approximation. Finally, we use the characteristic timescales of scattering for particles of different energies to discuss the feasibility of using such a system of tethers as a long and short‐term remediation solution.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.502
Threshold uncertainty score0.599

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.050
GPT teacher head0.363
Teacher spread0.314 · 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 designBench or experimental
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

Citations2
Published2016
Admission routes2
Has abstractyes

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