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Record W4394935970 · doi:10.1029/2024ja032505

A Simplified Method of True Height Analysis to Estimate the Real Height of Sporadic E Layers

2024· article· en· W4394935970 on OpenAlexaff
C. Haldoupis, Haris Haralambous, C. E. Meek

Bibliographic record

VenueJournal of Geophysical Research Space Physics · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsStatisticsGeodesyMathematicsEnvironmental scienceGeology

Abstract

fetched live from OpenAlex

Abstract The sporadic E (Es) layer virtual height and the ordinary wave critical frequency are routinely measured ionogram parameters, used to characterize the Es altitude occurrence and intensity. It has become common practice to take the real height to be about equal to by assuming that signal propagation delays in the E region plasma below the layer are small and can be neglected. Although this applies for nighttime, during daytime it may overestimate significantly. The present paper relies on true height analysis theory to devise a simplified method and propose an algorithm that can estimate Es real heights reasonably well. The method relies on and ionosonde measurements and E region electron density profiles obtained from the International Reference Ionosphere model. The algorithm is applied to a typical set of Digisonde observations to compute and examine real height variations and functional dependencies. Whereas ≃ at nighttime, during daytime there are notable − differences taking values less than 10 km for most of the observed layers. During the early morning and early afternoon hours, however, when weak layers appear at upper heights, the virtual to real height differences become larger reaching 20–25 km. The method proposed here for the estimation of can be easily applied to improve the accuracy of the results of sporadic E layer studies.

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)0.001
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.023
GPT teacher head0.401
Teacher spread0.378 · 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

Citations6
Published2024
Admission routes1
Has abstractyes

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