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Record W4390153247 · doi:10.1093/gji/ggad487

Local spherical harmonic power spectra from local magnetic or gravity data

2023· article· en· W4390153247 on OpenAlexaff
Alain Plattner, C. L. Johnson

Bibliographic record

VenueGeophysical Journal International · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGeomagnetism and Paleomagnetism Studies
Canadian institutionsUniversity of British Columbia
FundersNuclear Safety and Security CommissionNational Aeronautics and Space Administration
KeywordsSpectral lineHarmonicGeophysicsGeologyGeodesyPhysicsAcousticsAstronomy

Abstract

fetched live from OpenAlex

SUMMARY We present a method to calculate local spherical harmonic power spectra directly from spacecraft magnetic and gravity data with varying spacecraft altitude. Previously published applications of local spherical harmonic power spectra have been formulated for data that are available at a single collection altitude, such as data evaluated from a global spherical harmonic model. Our approach consists of first solving for local models from local data and then obtaining local multitaper spectra from the local models. We demonstrate with numerical tests that this approach can produce reliable results. Our method is particularly useful in situations where data coverage does not allow for calculating global magnetic or gravity field models, or where data quality or quantity varies regionally and where local models could yield superior resolution or quality over global models.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.611
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.001
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 head0.020
GPT teacher head0.274
Teacher spread0.253 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations7
Published2023
Admission routes1
Has abstractyes

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