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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 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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0240.008

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 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

Citations10
Published2021
Admission routes1
Has abstractyes

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