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Record W2205261797 · doi:10.1002/2015rs005808

Automatically determining the origin direction and propagation mode of high-frequency radar backscatter

2015· article· en· W2205261797 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueRadio Science · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Space AgencyNatural Environment Research CouncilSight Research UK
KeywordsBackscatter (email)AzimuthRadarElevation (ballistics)Remote sensingGeologyGeodesyElevation angleReflection (computer programming)IonosphereOpticsPhysicsComputer scienceGeophysicsTelecommunications

Abstract

fetched live from OpenAlex

Elevation angles of returned backscatter are calculated at Super Dual Auroral Radar Network radars using interferometric techniques. These elevation angles allow the altitude of the reflection point to be estimated, an essential piece of information for many ionospheric studies. The elevation angle calculation requires knowledge of the azimuthal return angle. This directional angle is usually assumed to lie along a narrow beam from the front of the radar, even though the signals are known to return from both in front of and behind the radar. If the wrong direction of return is assumed, large uncertainties will be introduced through the azimuthal return angle. This paper introduces a means of automatically determining the correct direction of arrival and the propagation mode of backscatter. The application of this method will improve the accuracy of backscatter elevation angle data and aid in the interpretation of both ionospheric and ground backscatter 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.

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 score0.443
Threshold uncertainty score0.185

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 head0.009
GPT teacher head0.246
Teacher spread0.236 · 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