MétaCan
Menu
Back to cohort
Record W3080315306 · doi:10.1029/2019je006293

Observations and Electron Density Retrievals of Jupiter's Discrete Auroral Arcs Using the Juno Microwave Radiometer

2020· article· en· W3080315306 on OpenAlexaff
Amorée Hodges, Paul G. Steffes, Amadeo Bellotti, J. H. Waite, Shannon Brown, Fabiano Oyafuso, Glenn S. Orton, J. K. Arballo, G. R. Gladstone, S. Levin, S. J. Bolton

Bibliographic record

VenueJournal of Geophysical Research Planets · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsLockheed Martin (Canada)
FundersSouthwest Research InstituteNational Aeronautics and Space Administration
KeywordsJupiter (rocket family)MicrowaveMicrowave radiometerPhysicsRadiometerAtmosphere (unit)Electron densityFlux (metallurgy)Atmosphere of JupiterElectronAstronomyJovianAtmospheric sciencesAstrophysicsPlanetMaterials scienceMeteorologyOptics

Abstract

fetched live from OpenAlex

Abstract Jupiter's aurorae reflect microwave radiation emitted upward from Jupiter's atmosphere and downward from the cold sky above due to regions in the auroral plasma with increased electron densities. The lack of thermal radiation from the atmosphere was observed by Juno's Microwave Radiometer (MWR) on overflights of the aurorae during seven different orbits. Out of Juno's first 21 orbits, seven orbits inferred enhanced electron densities in Jupiter's auroral arcs. The most profound disruption in microwave emission was observed during Perijove 5. This perijove demonstrated the most significant cold spot for Channel 1 (0.6 GHz), with cold spots also present in Channels 2 (1.25 GHz) and 3 (2.6 GHz) in a location where the influence of Jupiter's moon, Io, likely increased the electron density in Jupiter's aurora. The maximum electron densities retrieved from Channel 1 are on the order of 3 × 10 9 cm −3 , and in the presence of the Io flux tube, electron densities could reach 10 10 cm −3 affecting Channels 2 and 3.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.727
Threshold uncertainty score0.289

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.101
GPT teacher head0.349
Teacher spread0.247 · 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.

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

Citations8
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Geophysical Research PlanetsSame topicAstro and Planetary ScienceFrench-language works237,207