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Record W2088480119 · doi:10.1190/1.2057982

Correlation between aeromagnetic data rejection and geomagnetic indices

2005· article· en· W2088480119 on OpenAlexaffabout
Marc A. Vallée, Larry Newitt, Régis Dumont, Pierre Keating

Bibliographic record

VenueGeophysics · 2005
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsNatural Sciences and Engineering Research Council of CanadaGeological Survey of Canada
Fundersnot available
KeywordsEarth's magnetic fieldGeologyAeromagnetic surveyIndex (typography)GeodesyAmplitudeGeophysicsSeismologyMagnetic field

Abstract

fetched live from OpenAlex

Abstract Predicting the rejection of aeromagnetic data would be a useful tool for aeromagnetic survey planning. To relate aeromagnetic survey requirements to geomagnetic activity monitoring and prediction, we analyzed the relationship between the rejection of aeromagnetic data as it is measured during surveys and the variations in existing geomagnetic indices. The magnetic data were collected at Canadian magnetic observatories during 2001 and covered the polar cap, auroral, and subauroral zones. The geomagnetic indices were global and local indices. The global indices included the Kp, ap, and Dst indices. The local indices were the three-component hourly ranges, the three-component maximum rate of change, and the Pc3 pulsation index. The goodness of fit was used to compare the results between the different indices at different locations. In general, there was some correlation between global geomagnetic indices and the rate of rejection of aeromagnetic data. Good correlation with a global index was obtained with the daily mean of the Ap index for a station located in the subauroral zone. The best correlation was obtained with local indices and particularly with the Pc3 index amplitude. From these results we conclude that forecasting Pc3 index amplitude would be a useful tool for planning aeromagnetic surveys.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.893
Threshold uncertainty score0.686

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.228
Teacher spread0.218 · 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 designOther design
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

Citations6
Published2005
Admission routes2
Has abstractyes

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