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Record W1990053354 · doi:10.5194/angeo-27-1643-2009

Radar efficiency and the calculation of decade-long PMSE backscatter cross-section for the Resolute Bay VHF radar

2009· article· en· W1990053354 on OpenAlexaffabout
Nimalan Swarnalingam, W. K. Hocking, P. S. Argall

Bibliographic record

VenueAnnales Geophysicae · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsFanshawe CollegeWestern University
FundersSmithsonian Astrophysical ObservatoryTU Graz, Internationale Beziehungen und Mobilitätsprogramme
KeywordsRadarBackscatter (email)BayRemote sensingRadar cross-sectionLatitudeEnvironmental scienceGeologyGeodesyMeteorologyPhysicsTelecommunicationsComputer scienceOceanography

Abstract

fetched live from OpenAlex

Abstract. The Resolute Bay VHF radar, located in Nunavut, Canada (75.0° N, 95.0° W) and operating at 51.5 MHz, has been used to investigate Polar Mesosphere Summer Echoes (PMSE) since 1997. PMSE are a unique form of strong coherent radar echoes, and their understanding has been a challenge to the scientific community since their discovery more than three decades ago. While other high latitude radars have recorded strong levels of PMSE activities, the Resolute Bay radar has observed relatively lower levels of PMSE strengths. In order to derive absolute measurements of PMSE strength at this site, a technique is developed to determine the radar efficiency using cosmic (sky) noise variations along with the help of a calibrated noise source. VHF radars are only rarely calibrated, but determination of efficiency is even less common. Here we emphasize the importance of efficiency for determination of cross-section measurements. The significant advantage of this method is that it can be directly applied to any MST radar system anywhere in the world as long as the sky noise variations are known. The radar efficiencies for two on-site radars at Resolute Bay are determined. PMSE backscatter cross-section is estimated, and decade-long PMSE strength variations at this location are investigated. It was noticed that the median of the backscatter cross-section distribution remains relatively unchanged, but over the years a great level of variability occurs in the high power tail of the distribution.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.780
Threshold uncertainty score0.399

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 head0.250
Teacher spread0.243 · 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 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

Citations17
Published2009
Admission routes2
Has abstractyes

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