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Record W2170955317 · doi:10.1029/2007ja012322

An explanation for the seasonal dependence of midlatitude sporadic <i>E</i> layers

2007· article· en· W2170955317 on OpenAlexaff
C. Haldoupis, D. Pancheva, W. Singer, C. E. Meek, J. W. MacDougall

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2007
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsWestern UniversityUniversity of Saskatchewan
Fundersnot available
KeywordsSporadic E propagationThermosphereMiddle latitudesMeteoroidAtmospheric sciencesGeologyDeposition (geology)Meteor (satellite)SeasonalityIonosphereIonosondeAtmosphere (unit)Wind shearClimatologyGeophysicsMeteorologyPlasmaWind speedElectron densityPhysicsAstrobiologyOceanographyPaleontologyBiology

Abstract

fetched live from OpenAlex

The midlatitude sporadic E layers form when metallic ions of meteoric origin in the lower thermosphere are converged vertically in a wind shear. The occurrence and strength of sporadic E follow a pronounced seasonal dependence marked by a conspicuous summer maximum. Although this is known since the early years of ionosonde studies, its cause has remained a mystery as it cannot be accounted for by the windshear theory of E s formation. We show here that the marked seasonal dependence of sporadic E correlates well with the annual variation of sporadic meteor deposition in the upper atmosphere. The later has been established recently from long‐term measurements using meteor radar interferometers in the Northern and Southern Hemispheres. Knowing that the occurrence and strength of sporadic E layers depends directly on the metal ion content, which apparently is determined primarily by the meteoric deposition, the present study offers a cause‐and‐effect explanation for the long‐going mystery of sporadic E layer seasonal dependence.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.032
GPT teacher head0.341
Teacher spread0.309 · 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 source (direct Gemma or distilled Codex), 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

Citations162
Published2007
Admission routes1
Has abstractyes

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