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Record W4385507242 · doi:10.1016/j.icarus.2023.115740

Long-term variability of Jupiter’s northern auroral 8- <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si198.svg" display="inline" id="d1e1365"> <mml:mi mathvariant="normal">μ</mml:mi> </mml:math> m CH <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si9.svg" display="inline" id="d1e1370"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>4</mml:mn> </mml:mrow> </mml:msub> </mml:math> emissions

2023· article· lv· W4385507242 on OpenAlexfundno aff
James Sinclair, Robert A. West, J. Barbara, Chihiro Tao, Glenn S. Orton, T. K. Greathouse, Rohini Giles, Denis Grodent, Leigh N. Fletcher, P. G. J. Irwin

Bibliographic record

VenueIcarus · 2023
Typearticle
Languagelv
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsnot available
FundersH2020 European Research CouncilHorizon 2020European Research CouncilScience and Technology Facilities CouncilNational Research Council CanadaJapan Society for the Promotion of ScienceHorizon 2020 Framework ProgrammeMinisterio de Ciencia, Tecnología e Innovación ProductivaAgencia Nacional de Investigación y DesarrolloJet Propulsion LaboratoryMinistry of Education, Culture, Sports, Science and TechnologyKorea Astronomy and Space Science InstituteNational Aeronautics and Space AdministrationCalifornia Institute of TechnologyW. M. Keck FoundationUniversity of LeicesterUniversity of CaliforniaMinistério da Ciência, Tecnologia, Inovações e ComunicaçõesNational Science Foundation
KeywordsRadianceAtmospheric sciencesJupiter (rocket family)LatitudeSolar cyclePhysicsSolar windEnvironmental scienceAstrophysicsGeologyAstronomyRemote sensingPlasma

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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 score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.244
Teacher spread0.227 · 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

Citations5
Published2023
Admission routes1
Has abstractno

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