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Record W4297671320 · doi:10.46620/rfi22-012

Characterization of the RFI Environment at the DRAO: The Classical Approach

2022· article· en· W4297671320 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsnot available
FundersSmithsonian Astrophysical Observatory
KeywordsCharacterization (materials science)Computer scienceEnvironmental scienceMaterials scienceNanotechnology

Abstract

fetched live from OpenAlex

The location of the Dominion Radio Astrophysical Observatory (DRAO) in interior British Columbia, Canada, was selected in part because of the expectation that the surrounding mountain ranges should shield the site against radio frequency interference (RFI) from terrestrial sources. In recent years, a number of radio telescopes have been hosted at DRAO. Since August 2021, the RFI novelty detection (RFInd) site monitor has been collecting data in order to characterize the RFI environment at DRAO and to exploit machine learning to identify hidden RFI signatures in the observations. The current configuration of the RFInd site monitor covers the frequency range from 350-1800 MHz for a single polarization with time resolution of 950 ms and frequency resolution of 3.33 kHz. The observations include hourly hot-cold calibration cycles. In the following we explain how classical methods have been used to process the collected RFI data and to analyze them with the objective of using the derived RFI information in the design of new radio astronomical instruments.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.816
Threshold uncertainty score0.476

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 head0.011
GPT teacher head0.175
Teacher spread0.164 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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