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Record W1626597280 · doi:10.1029/2004rs003123

Legendre coding for digital ionosondes

2005· article· en· W1626597280 on OpenAlexafffundabout
Jun Huang, J. W. MacDougall

Bibliographic record

VenueRadio Science · 2005
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLegendre polynomialsAmbiguity functionIonosondeAutocorrelationComputer scienceRadarAlgorithmPulse repetition frequencyAcousticsMathematicsTelecommunicationsWaveformPhysicsStatisticsMathematical analysis

Abstract

fetched live from OpenAlex

A 1019 bit Legendre code was evaluated for use in digital ionosondes. Experimental testing was done using the Canadian Advanced Digital Ionosonde (CADI). Theoretically, the 1019 Legendre code autocorrelation function of this new sequence has very low peak sidelobe level of −32.6 dB, and the system signal‐to‐noise‐ratio (SNR) will be improved by 30 dB compared with a single pulse code. Field experiments were done near London, Ontario, Canada, using two CADIs in a bistatic arrangement with 20 km spacing. The experimental results agreed with the theoretical estimation (with ∼1 dB error), but a 10 Hz frequency difference between the two computers' reference frequencies, which showed up as a Doppler shift in ambiguity function, necessitated additional signal processing to get optimal performance. The experimental measurements showed that the system was able to get ionospheric echoes with very low power transmission (1 W peak) at a quiet receiving site because of the high system SNR.

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)0.001
metaresearch head score (Gemma)0.004
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: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.011
GPT teacher head0.226
Teacher spread0.215 · 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
GenreMethods

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

Citations16
Published2005
Admission routes3
Has abstractyes

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