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Record W2773090813 · doi:10.1139/cjp-2017-0460

Geomagnetic field <i>H</i>, <i>Z</i>, and electromagnetic induction features of coronal mass ejections in association with geomagnetic storm at African longitudes

2017· article· en· W2773090813 on OpenAlexvenueno aff
E.O. Falayi, J. O. Adepitan, O. A. Oyebanjo

Bibliographic record

VenueCanadian Journal of Physics · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSolar and Space Plasma Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsEarth's magnetic fieldGeomagnetic stormCoronal mass ejectionGeophysicsGeomagnetic secular variationSolar cycle 24Ionospheric dynamo regionMagnetic fieldAtmospheric sciencesSolar wind

Abstract

fetched live from OpenAlex

The largest geomagnetic disturbance caused by a coronal mass ejection (CME) of solar cycle 24 recorded on both 17 March and 22 June 2015 with minimum disturbance storm time values of −223 and −195 nT, respectively, was investigated. This study examines the effect of CME on Earth’s geomagnetic field, which includes the time derivatives of horizontal (H) and vertical (Z) components of the geomagnetic field and the rate of induction ΔZ/ΔH at African longitudes (AAE, MBO, HBK, HER, and TAM). The results demonstrated enhancement of dH/dt and dZ/dt in the daytime over the equatorial zone (AAE and MBO) and mid-latitudes (TAM, HER, and HBK) on 17 March 2015. Nighttime enhancement was observed on 22 June 2015 over the equatorial zones and mid-latitudes. Wavelet spectrum approach is used to investigate ΔZ/ΔH variation observed at AAE, MBO, HBK, HER, and TAM. The CME may have influence on time derivatives of geomagnetic field H, Z, and electromagnetic induction at the African longitudes, which may be associated with perturbations in electric fields and currents in the equatorial and low-latitude magnetic field linked with the changes in magnetospheric convection.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.992

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 head0.005
GPT teacher head0.195
Teacher spread0.190 · 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 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

Citations2
Published2017
Admission routes1
Has abstractyes

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