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Record W3043357567 · doi:10.1186/s40623-020-01234-x

Relative contributions of large-scale and wedgelet currents in the substorm current wedge

2020· article· en· W3043357567 on OpenAlexaff
Y. Nishimura, L. R. Lyons, Christine Gabrielse, J. M. Weygand, E. Donovan, V. Angelopoulos

Bibliographic record

VenueEarth Planets and Space · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Calgary
FundersAir Force Office of Scientific ResearchU.S. Air ForceNuclear Safety and Security CommissionNational Aeronautics and Space AdministrationNational Science Foundation
KeywordsSubstormWedge (geometry)GeologyCurrent (fluid)GeophysicsGeodesyScale (ratio)SeismologyPhysicsGeometryMathematicsMagnetosphereOceanographyMagnetic field

Abstract

fetched live from OpenAlex

Abstract We examined how much large-scale and localized upward and downward currents contribute to the substorm current wedge (SCW), and how they evolve over time, using the THEMIS all-sky imagers (ASIs) and ground magnetometers. One type of events is dominated by a single large-scale wedge, with upward currents over the surge and broad downward currents poleward-eastward of the surge. The other type of events is a composite of large-scale wedge and wedgelets associated with streamers, with each wedgelet having comparable intensity to the large-scale wedge currents. Among 17 auroral substorms with wide ASI coverage, the composite current type is more frequent than the single large-scale wedge type. The dawn–dusk size of each wedgelet is ~ 600 km in the ionosphere (~ 3.2 RE in the magnetotail, comparable to the flow channel size). We suggest that substorms have more than one type of SCW, and the composite current type is more frequent.

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)0.000
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.235
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

Citations23
Published2020
Admission routes1
Has abstractyes

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