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Record W7095219332

UNCLASSIFIED/UNLIMITED UNCLASSIFIED/UNLIMITED VLF Phase Perturbations Produced by the Variability in Large (V/m) Mesospheric Electric Fields in the 60 – 70 km Altitude Range

2015· article· en· W7095219332 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicAcademic Publishing and Open Access
Canadian institutionsnot available
Fundersnot available
KeywordsElectric fieldIonosphereAltitude (triangle)Phase (matter)Atmospheric electricityRange (aeronautics)Field (mathematics)
DOInot available

Abstract

fetched live from OpenAlex

The large (V/m) mesospheric electric fields have been identified as a possible cause of VLF phase perturbations. These fields affect the fundamental processes that govern the lower D region parameters, primarily the electron temperature and effective collision frequency. The main ionospheric parameter needed to calculate VLF phase perturbations is the low-frequency electron plasma conductivity. All the electric field data available to 1990 were collected with electric field sensors on board more than 50 rockets launched over approximately 30 years in the USSR and the U.S.A., which were insufficient to address VLF phase perturbations. This paper discusses the progress made in addressing large (V/m) mesospheric electric fields between 60- and 70-km altitudes since 1990. It focuses on achieving the breakthrough, the development of a radio wave technique for sensing large electric fields remotely by using MF radar, and on the fact that the electric field variability leads to the variability of ionospheric conduction contours by a few kilometers in altitude. The statistical analysis of the large mesospheric electric field data acquired in the 60- and 67-km altitude region in Canada and Ukraine suggests that large mesospheric electric fields may occur during about 70 % of all the time. However, reasonable assessments of VLF phase perturbations need information on the temporal and especially spatial

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)0.036
metaresearch head score (Gemma)0.045
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication, Open science
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.480
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0360.045
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.009
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0060.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.127
GPT teacher head0.394
Teacher spread0.267 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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