MétaCan
Menu
Back to cohort
Record W3157580026 · doi:10.1142/s2251171721500070

Radio-Frequency Interference at the McGill Arctic Research Station

2021· article· en· W3157580026 on OpenAlexafffundabout
T. A. Dyson, H. C. Chiang, E. Egan, N. Ghazi, Tamara Ménard, Raúl A. Monsalve, T. Moso, J. B. Peterson, Jonathan Sievers, Simon Tartakovsky

Bibliographic record

VenueJournal of Astronomical Instrumentation · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadio Astronomy Observations and Technology
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsMars Exploration ProgramElectromagnetic interferenceArcticEnvironmental scienceRemote sensingMeteorologyIonosphereThe arcticAntenna (radio)Computer scienceTelecommunicationsPhysicsGeographyAstronomyGeology

Abstract

fetched live from OpenAlex

The frequencies of interest for redshifted 21[Formula: see text]cm observations are heavily affected by terrestrial radio-frequency interference (RFI). We identify the McGill Arctic Research Station (MARS) as a new RFI-quiet site and report its RFI occupancy using 122[Formula: see text]h of data taken with a prototype antenna station developed for the Array of Long-Baseline Antennas for Taking Radio Observations from the Sub-Antarctic. Using an RFI flagging process tailored to the MARS data, we find an overall RFI occupancy of 1.8% averaged over 20–125[Formula: see text]MHz. In particular, the FM broadcast band (88–108[Formula: see text]MHz) is found to have an RFI occupancy of at most 1.6%. The data were taken during the Arctic summer, when degraded ionospheric conditions and an active research base contributed to increased RFI. The results quoted here therefore represent the maximum-level RFI environment at MARS.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.373
Threshold uncertainty score0.742

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.002

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 head0.048
GPT teacher head0.322
Teacher spread0.274 · 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 designObservational
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

Citations7
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Astronomical InstrumentationSame topicRadio Astronomy Observations and TechnologyFrench-language works237,207