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Record W4392581619 · doi:10.5194/egusphere-egu24-12382

Ten Years of Low-Earth Orbit Observations from CASSIOPE/Swarm-Echo

2024· preprint· en· W4392581619 on OpenAlexaff
Andrew Howarth, A. W. Yau, P. A. Bernhardt, Gordon James, D. J. Knudsen, Richard B. Langley, David M. Miles

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicSpace Satellite Systems and Control
Canadian institutionsUniversity of New BrunswickUniversity of Calgary
Fundersnot available
KeywordsEcho (communications protocol)Low earth orbitSwarm behaviourOrbit (dynamics)AstrobiologyPhysicsAstronomyComputer scienceEngineeringArtificial intelligenceAerospace engineeringSatellite

Abstract

fetched live from OpenAlex

From the vantage point of an elliptic, polar, low-earth orbit (81o inclination, 325 km x 1500 km initial apogee/perigee), CASSIOPE/Swarm-Echo has been observing the ionosphere-thermosphere system for over ten years. The Enhanced Polar Outflow Probe (e-POP) payload onboard collects data on space weather and related phenomena, including measurements of the local magnetic field, low-energy ion and electron energy distributions, high-frequency radio waves (natural and man-made), GPS signals, and aurora. These observations from a non-sun-synchronous orbit over a range of altitudes constitutes a unique data set that allows for investigation of the earth’s magnetic field and related current systems, upper atmospheric dynamics, auroral dynamics, and related coupling processes among the magnetosphere, ionosphere, thermosphere, and plasmasphere. This presentation will highlight the discoveries of the ten years of e-POP operation, including recent work on plasma waves generated by moving charged space objects and machine-learning techniques applied to analysis of magnetic field data and auroral images. We will also present some of the new Swarm-Echo data products and system tools available for use and look at the future direction of both the mission and the evolving data set.

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 score0.146
Threshold uncertainty score0.956

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 integrity0.0000.001
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 head0.017
GPT teacher head0.214
Teacher spread0.197 · 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
Published2024
Admission routes1
Has abstractyes

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