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Photoelectron Transport Model and Comparison with Satellite Observations

2024· preprint· en· W4404053462 on OpenAlexaff
R. Rankin, Dmytro Sydorenko, Jing Liang, E. Donovan

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsSpacecraftIonosphereMagnetosphereSatelliteSolar windSpace weatherPhysicsAerospace engineeringMagnetopauseBow shock (aerodynamics)Remote sensingComputer scienceGeophysicsMeteorologyAstronomyShock waveGeologyPlasma

Abstract

fetched live from OpenAlex

The Solar Wind Magnetosphere Ionosphere Link Explorer (SMILE) is a future spacecraft mission supported by the European Space Agency and the Chinese Academy of Science. It is expected that the mission will be launched in 2025 by ESA. SMILE will target the magnetopause, cusp, and bow shock regions. To investigate magnetosphere-ionosphere coupling, the spacecraft will take auroral images using an ultraviolet imager (UVI). For scientific support and analysis of data collected by the UVI, a numerical model of the ionosphere and UV emission has been developed, which is presented in the poster presentation. The photoelectron transport module of the model is described in the poster. The SMILE UV-imager will take global auroral images that for calibration, require specification of the changing state of the ionosphere. We demonstrate progress in meeting this goal and showcase using an example how the model works.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.017
GPT teacher head0.239
Teacher spread0.222 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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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