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Record W2889674466 · doi:10.1109/lgrs.2018.2856185

High-Frequency Over-the-Horizon Radar in Canada

2018· article· en· W2889674466 on OpenAlexaffabout
T. Thayaparan, Yousef Ibrahim, John Polak, R. J. Riddolls

Bibliographic record

VenueIEEE Geoscience and Remote Sensing Letters · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsQueen's UniversityUniversity of WaterlooDefence Research and Development Canada
Fundersnot available
KeywordsOver-the-horizon radarRadarRadar trackerRay tracing (physics)Remote sensingComputer scienceFire-control radarSkywaveIonosphere3D radarTracking (education)Range (aeronautics)Radar engineering detailsGeologyRadar imagingTelecommunicationsAerospace engineeringGeophysicsEngineeringPhysicsOptics

Abstract

fetched live from OpenAlex

Over-the-horizon radars (OTHRs) have recently been making a comeback in Canada. As the need for accurate long-range tracking becomes more important, less-expensive ground-based radars are once again being considered for more effective long-range surveillance of Canadian airspace. Ray tracing is a powerful tool and is, especially, useful in applications requiring a detailed knowledge of radio wave propagation through the ionosphere. In this letter, new methods are developed to determine the feasible radar parameters such as operating frequencies, elevation angles, and absorption for OTHR operation using a 3-D ray tracing technique and up-to-date ionospheric, magnetic, and absorption models. The results of these simulations can be used for frequency monitoring systems and other OTHR applications in Canada.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.004
GPT teacher head0.190
Teacher spread0.186 · 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 designNot applicable
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

Citations37
Published2018
Admission routes2
Has abstractyes

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Same venueIEEE Geoscience and Remote Sensing LettersSame topicIonosphere and magnetosphere dynamicsFrench-language works237,207