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Record W1837083641 · doi:10.1002/2015ja021707

Multispacecraft current estimates at swarm

2015· article· en· W1837083641 on OpenAlexaff
M. W. Dunlop, Yanyan Yang, Jiade Yang, H. Lühr, Chao Shen, Nils Olsen, P. Ritter, Qinghe Zhang, Jinbin Cao, H. S. Fu, Roger Haagmans

Bibliographic record

VenueJournal of Geophysical Research Space Physics · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsCascades (Canada)
FundersNational Key Research and Development Program of ChinaScience and Technology Facilities CouncilNational Natural Science Foundation of ChinaNatural Environment Research CouncilSight Research UK
KeywordsCurrent (fluid)Swarm behaviourPhysicsGeologyGeographyEcologyBiologyThermodynamics

Abstract

fetched live from OpenAlex

Abstract During the first several months of the three‐spacecraft Swarm mission all three spacecraft came repeatedly into close alignment, providing an ideal opportunity for validating the proposed dual‐spacecraft method for estimating current density from the Swarm magnetic field data. Two of the Swarm spacecraft regularly fly side‐by‐side in closely similar orbits, while the third at times approaches the other two. This provides a data set which under certain assumptions of stationarity of the magnetic field can produce 2, 3, 4, 5 (or more) point measurements, which can be cross compared. We find that at low Earth orbit the use of time‐shifted positions allow stable estimates of current density to be made and can verify temporal effects as well as validating the interpretation of the current components as arising predominantly from field‐aligned currents. In the case of four‐spacecraft configurations we can resolve the full vector current and therefore can check the perpendicular as well as parallel current density components directly, together with the quality factor for the estimates directly (for the first time in situ at low Earth orbit).

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.351
Threshold uncertainty score0.885

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.058
GPT teacher head0.371
Teacher spread0.313 · 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 designTheoretical or conceptual
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

Citations36
Published2015
Admission routes1
Has abstractyes

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