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Ionospheric ions in the magnetosphere: Important at large and small scales

2020· article· en· W3164405264 on OpenAlexaff
M. André, Sergio Toledo‐Redondo, A. W. Yau

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPhysicsIonosphereIonMagnetosphereAmperePlasmaGeophysicsQuantum mechanics

Abstract

fetched live from OpenAlex

Cold (eV) ions of ionospheric origin dominate the number density of most of the volume of the magnetosphere during most of the time. Supersonic flows of cold positive ions are common and can cause a negatively charged wake behind a positively charged spacecraft. The associated induced electric field can be observed and can be used to study the cold ions. We present observations from the Cluster and MMS spacecraft showing how a charged satellite, and also individual charged wire booms of an electric field instrument, can be used to investigate cold ion populations. Ionospheric ions affect large scales, including the Alfvén velocity and thus energy transport with waves and the magnetic reconnection rate. These ions also affect small-scale kinetic plasma physics, including the Hall physics and wave instabilities associated with magnetic reconnection. Concerning large scales, we summarize observations from several spacecraft and show that a typical total outflow rate of ionospheric ions is 1026 ions/s and that many of these ions stay cold also after a long time in the magnetosphere. Concerning small scales, we show examples of how cold ions modify the Hall physics of thin current sheets, including magnetic reconnection separatrices. On small kinetic scales the cold ions introduce a new length-scale, a gyro radius between the gyro radii of hot (keV) ions and electrons. The Hall currents carried by electrons can be partially cancelled by the cold ions when electrons and the magnetized cold ions ExB drift together. Also, close to a reconnection X-line an additional diffusion region can be formed (regions associated with hot and cold ions, and with electrons, total of three).

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.009
GPT teacher head0.203
Teacher spread0.195 · 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
Published2020
Admission routes1
Has abstractyes

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