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Record W4388569128 · doi:10.1063/5.0142896

Potential impacts of hydrogen band EMIC waves on the ion velocity distributions: MMS observations

2023· article· en· W4388569128 on OpenAlexaff
Abdullah Khan, A. A. Abid, M. S. Hussain, M. N. S. Qureshi, Shahid Mehmood, Amin Esmaeili

Bibliographic record

VenuePhysics of Plasmas · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsPhysicsMagnetosphereAtomic physicsEmic and eticHydrogenIonProtonHeliumCyclotronPlasmaNuclear physics

Abstract

fetched live from OpenAlex

In this paper, the influence of the hydrogen (H+) band electromagnetic ion cyclotron (EMIC) waves on the hydrogen and helium velocity distributions has been studied. The hydrogen band EMIC waves have been investigated in the inner magnetosphere using the magnetospheric multiscale mission. The EMIC waves for the frequency range typical frequency have been frequently observed in the Earth's magnetosphere and have received considerable attention for energy transport across the magnetosphere. In this manuscript, we studied the velocity distribution of cold/hot proton and helium ions at different times of the event under consideration. For cold (1–600 eV) hydrogen ions, the velocity distribution is directly proportional to the growth rate of the EMIC wave, whereas the hot (1–40 keV) hydrogen ions have a ring distribution, which are not strongly influenced by the growth of EMIC waves like cold hydrogen ions, but the helium (1 eV–40 keV) ions are rarely influenced by EMIC waves.

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)0.001
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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

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 head0.016
GPT teacher head0.230
Teacher spread0.214 · 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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