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Record W4322010590 · doi:10.5194/egusphere-egu23-9101

Understanding Ionospheric Conditions using the e-POP on Swarm-E

2023· preprint· en· W4322010590 on OpenAlexaff
E. Ceren Kalafatoglu Eyiguler, D. W. Danskin, Kuldeep Pandey, G. C. Hussey, R. G. Gillies, A. W. Yau

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of CalgaryUniversity of Saskatchewan
Fundersnot available
KeywordsIonosphereTransmitterPayload (computing)Swarm behaviourPhysicsRemote sensingNarrowbandMagnetometerComputer scienceGeophysicsAcousticsAerospace engineeringOpticsGeologyTelecommunicationsEngineeringChannel (broadcasting)Magnetic field

Abstract

fetched live from OpenAlex

Swarm-E carries the e-POP payload, which consists of eight scientific instruments. The Radio Receiver Instrument (RRI) on e-POP may be used to detect density structures that modify the characteristics of transionospheric HF radio waves from ground-based transmitters. RRI has a cross-dipole antenna designed to determine the polarisation characteristics of the incident radio waves. Joint experiments with other instruments, IRM (Imaging and Rapid-scanning ion Mass spectrometer) and MGF (Fluxgate Magnetometer), complement RRI for better understanding of the local ionosphere conditions and integrated conditions along the path between a ground-based transmitter and Swarm-E. Under optimum conditions, the RRI determines the full polarisation characteristics. This presentation discusses past and future contributions to ionospheric science of the Swarm constellation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.142
GPT teacher head0.311
Teacher spread0.169 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2023
Admission routes1
Has abstractyes

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