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Record W4386361014 · doi:10.3847/25c2cfeb.abafe08b

Global Networks of Ground-Based Magnetometers Enable Cutting-Edge Heliophysics Research, Education, and Space Weather Operations

2023· article· en· W4386361014 on OpenAlexaff
Michael D. Hartinger, M. J. Engebretson, G. Lu, Martin Connors, Ryan McGranaghan, E. Joshua Rigler, Xueling Shi, J. M. Weygand, Adam Schultz, Hyomin Kim, Michelle Salzano, Chigomezyo M. Ngwira, A. W. Smith, Jason Derr, Doğacan Öztürk, Shane Coyle, A. P. Dimmock, D. L. Turner, Peter Chi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsAthabasca University
Fundersnot available
KeywordsMagnetometerEnhanced Data Rates for GSM EvolutionSpace weatherSpace (punctuation)Aerospace engineeringRemote sensingComputer scienceMeteorologyEnvironmental scienceGeodesyPhysicsEngineeringGeologyTelecommunicationsMagnetic fieldOperating system

Abstract

fetched live from OpenAlex

Whitepaper #162 in the Decadal Survey for Solar and Space Physics (Heliophysics) 2024-2033. Main topics: space weather applications; basic research. Additional topics: space weather research/operations/research loop; space weather research/applications/operations pipeline; […]

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.026
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0260.009

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 head0.014
GPT teacher head0.288
Teacher spread0.274 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2023
Admission routes1
Has abstractyes

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