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Record W3211114642 · doi:10.1029/2021gl095432

Space Weather Observations With InSight

2021· article· en· W3211114642 on OpenAlexaff
Anna Mittelholz, C. L. Johnson, Matthew Fillingim, S. P. Joy, J. R. Espley, J. S. Halekas, S. E. Smrekar, W. B. Banerdt

Bibliographic record

VenueGeophysical Research Letters · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsUniversity of British Columbia
FundersInstitut de Physique du Globe de ParisCentre National de la Recherche ScientifiqueEidgenössische Technische Hochschule ZürichCentre National d’Etudes SpatialesBranco Weiss Fellowship – Society in ScienceMax-Planck-GesellschaftUK Space AgencyNational Aeronautics and Space Administration
KeywordsCoronal mass ejectionSolar windSpace weatherMars Exploration ProgramPhysicsMagnetometerPlanetMagnetic cloudInterplanetary magnetic fieldMagnetic fieldGeophysicsMercury's magnetic fieldAstrobiologySolar SystemAstronomyAtmospheric sciences

Abstract

fetched live from OpenAlex

Abstract Solar activity, in the form of coronal mass ejections and corotating interaction regions, results in changes in the solar wind that propagate out through the solar system and interact with the magnetic field environments of planets. Such phenomena have been observed to affect the magnetic field and plasma around Mars as seen from orbit. However, no surface observations have previously been possible because of the absence of ground‐based instrumentation. Here, for the first time, we observe the effects of increased solar activity with the magnetometer on the InSight mission in December 2020. We find several days of increased activity including magnetic field fluctuations at periods of minutes to hours. Although only the flanks of this relatively weak coronal mass ejection hit Mars, the observed effects provide insight into how solar activity alters magnetic fields at the surface.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.705
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.061
GPT teacher head0.292
Teacher spread0.231 · 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 teacher head, not a consensus.

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".

Quick stats

Citations10
Published2021
Admission routes1
Has abstractyes

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