MétaCan
Menu
Back to cohort

Beneficial Impact of Rocket Engine Burns Over Ground VLF Transmitters for Radiation Belt Remediation

2025· article· en· W4408222573 on OpenAlexaffabout
P. A. Bernhardt, J. Baumgardner, Bengt Eliasson, Jacob Bortnik, A.D. Howarthy

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCombustion and Detonation Processes
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsRocket (weapon)Environmental remediationEnvironmental scienceAerospace engineeringVan Allen radiation beltAstrobiologyAutomotive engineeringRemote sensingEngineeringGeologyPhysicsMagnetosphereContaminationNuclear physics

Abstract

fetched live from OpenAlex

Currently, there are about 10,000 active space objects in orbit around the earth. Satellites have to avoid collisions by energetic particles that can damage solar panels and electronic components. Whistler and electromagnetic ion cyclotron (EMIC) can scatter trapped radiation into the atmospheric loss cone and, thus, reduce energetic particle fluxes. The proliferation of satellite launches and International Space Station (ISS) reboost missions may be used to reduce the populations of harmful radiation. Low frequency (ELF/VLF) waves can be artificially intensified to produce strong electromagnetic (EM) waves (e.g., whistlers). A Rocket Exhaust Driven Amplification (REDA) system has been tested with (1) a ground Navy ELF/VLF transmitter used as coherent wave source, (2) the firing a rocket motor over the transmitter to produce an amplifying medium in the ionosphere and (3) the satellite plasma-wave sensor on the Canadian SWARM-E. Measured wave amplitudes can impact the dynamics in the inner magnetosphere and reduce the lifetime of the radiation belts.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.512
Threshold uncertainty score0.336

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 head0.260
Teacher spread0.254 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same topicCombustion and Detonation ProcessesFrench-language works237,207