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Record W2003949356 · doi:10.5194/angeo-25-2529-2007

Energy deposition in the ionosphere through a global field line resonance

2007· article· en· W2003949356 on OpenAlexafffund
I. J. Rae, C. E. J. Watt, F. R. Fenrich, I. R. Mann, L. G. Ozeke, A. Kale

Bibliographic record

VenueAnnales Geophysicae · 2007
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Alberta
FundersCanadian Space Agency
KeywordsSubstormIonosphereJoule heatingPhysicsField lineMagnetosphereGeophysicsComputational physicsLongitudeResonance (particle physics)DissipationSolar windAtmospheric sciencesLatitudePlasmaAtomic physicsAstronomy

Abstract

fetched live from OpenAlex

Abstract. We present an interval whereby we can estimate the energy dissipation in the ionosphere through an externally-driven field line resonance. In this paper, we utilise an interval described in general by Rae et al. (2005), where the global magnetospheric cavity was shown to be energised via a high solar wind speed stream. Using the ground-based instrumentation available, we estimate the spatial extent of the generated pulsations to be at least 10° in latitude and 65° in longitude, a sizeable fraction of the dusk-sector ionosphere. Using a fortuitous conjunction with the Polar spacecraft, we compare point measurements of the net downward Poynting vector to the estimated Joule heating rate in the ionosphere, and find that model values of the Pedersen conductance are reasonable. In the interval of interest, we estimate the total dissipation rate during a global field line resonance to be comparable to that reported in substorm studies. Previous studies have estimated the total energy deposition via field line resonance to be up to 4% of that deposited during a small substorm. However, in this paper we find that the total energy deposited via Joule heating may actually be 30% or more of the energy deposited in the ionosphere during a substorm cycle using a conservative estimate of the pulsation duration.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.573
Threshold uncertainty score0.690

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 head0.007
GPT teacher head0.245
Teacher spread0.238 · 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

Citations49
Published2007
Admission routes2
Has abstractyes

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