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Record W2129261784 · doi:10.5194/acp-6-5307-2006

Isolated lower mesospheric echoes seen by medium frequency radar at 70° N, 19° E

2006· article· en· W2129261784 on OpenAlexaff
Chris Hall, A. H. Manson, C. E. Meek, Satonori Nozawa

Bibliographic record

VenueAtmospheric chemistry and physics · 2006
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMesosphereRadarEcho (communications protocol)Altitude (triangle)GeologyPhysicsAtmospheric sciencesRemote sensingStratosphereGeometryComputer scienceMathematics

Abstract

fetched live from OpenAlex

Abstract. We have noted sporadic instances of strong isolated reflections of medium frequency (MF) radar waves from the mesosphere from as low as 50 km altitude and have devised a set of criteria for isolating these apparently anomalous echoes from those normally occurring from progressive partial reflections in the D-region. The object of this study is to map the occurrences of such echoes facilitating comparisons with other observations. For example, the similarity and simultaneity of the echo structure for the 20 January 2005 with VHF radar results presented by Lübken et al. (2006) are particularly striking. In presenting a number of such echo events since 2001 selected from the MF radar dataset (which spans 1997 to present), we find that virtually all echo occurrences coincide with enhanced solar proton fluxes suggesting that substantial ionisation of the mesosphere is a necessary condition. Strong partial reflections of the radio wave in the lower mesosphere combined with seasonally varying total absorption higher up, thus giving false impressions of lower mesospheric layers preferentially in winter, constitute a scenario consistent with our observations.

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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.002
GPT teacher head0.178
Teacher spread0.176 · 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

Citations14
Published2006
Admission routes1
Has abstractyes

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