Characterization of the RFI Environment at the DRAO: The Classical Approach
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".