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Record W2577281329 · doi:10.1002/2016ja022571

Thermal ion imagers and Langmuir probes in the Swarm electric field instruments

2017· article· en· W2577281329 on OpenAlexaff
D. J. Knudsen, J. K. Burchill, S. Buchert, A. I. Eriksson, R. Gill, Jan‐Erik Wahlund, L. Åhlén, M. S. Smith, Brian Moffat

Bibliographic record

VenueJournal of Geophysical Research Space Physics · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of WaterlooCOM DEV InternationalUniversity of Calgary
Fundersnot available
KeywordsLangmuir probeElectric fieldSwarm behaviourPhysicsPlasmaMagnetic fieldIonosphereIonPlasma diagnosticsDrift velocityComputational physicsElectric potentialElectron temperatureAtomic physicsGeophysicsVoltage

Abstract

fetched live from OpenAlex

Abstract The European Space Agency's three Swarm satellites were launched on 22 November 2013 into nearly polar, circular orbits, eventually reaching altitudes of 460 km (Swarm A and C) and 510 km (Swarm B). Swarm's multiyear mission is to make precision, multipoint measurements of low‐frequency magnetic and electric fields in Earth's ionosphere for the purpose of characterizing magnetic fields generated both inside and external to the Earth, along with the electric fields and other plasma parameters associated with electric current systems in the ionosphere and magnetosphere. Electric fields perpendicular to the magnetic field are determined through ion drift velocity and magnetic field measurements via the relation . Ion drift is derived from two‐dimensional images of low‐energy ion distribution functions provided by two Thermal Ion Imager (TII) sensors viewing in the horizontal and vertical planes; is corrected for spacecraft potential as determined by two Langmuir probes (LPs) which also measure plasma density ne and electron temperature Te. The TII sensors use a microchannel‐plate‐intensified phosphor screen imaged by a charge‐coupled device to generate high‐resolution distribution images (66 × 40 pixels) at a rate of 16 s−1. Images are partially processed on board and further on the ground to generate calibrated data products at a rate of 2 s−1; these include , , and ion temperature Ti in addition to electron temperature Te and plasma density ne from the LPs.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.020
GPT teacher head0.324
Teacher spread0.304 · 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 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".

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Citations291
Published2017
Admission routes1
Has abstractyes

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