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Record W4321211463 · doi:10.1029/2022rs007591

Borealis: An Advanced Digital Hardware and Software Design for SuperDARN Radar Systems

2023· article· en· W4321211463 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.

Bibliographic record

VenueRadio Science · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsRadarComputer scienceSoftwareUpgradeRemote sensingRadar imagingRadar systemsRadar lock-onReal-time computingComputer hardwareContinuous-wave radarTelecommunicationsGeographyOperating system

Abstract

fetched live from OpenAlex

Abstract The Borealis radar system is a hardware and software upgrade to the conventional Super Dual Auroral Radar Network radar system, which has been used since the early 1990s. The conventional system has hardware and software that is aging, and many components are no longer supported. Limitations of the conventional system limit radar and data techniques for scientific discovery. Using software defined radios, Borealis has improved the flexibility, capabilities, and security of the radar system. Borealis has improved system monitoring and diagnostics and enables more complex experiments. Borealis provides improvements in spatial and temporal resolution. The system can perform full field‐of‐view imaging, pulse phase encoding and simultaneous multi‐frequency operations. With Borealis, data quality and system reliability has been improved. New radar and signal processing techniques are in development to further improve the capabilities of the system and of the data quality.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.846
Threshold uncertainty score0.485

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 communication0.0000.001
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 head0.013
GPT teacher head0.242
Teacher spread0.230 · 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