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RFI Issues for the next generation Very Large Array (ngVLA)

2023· article· en· W4389271611 on OpenAlexaboutno aff
Bryan Butler, Urvashi Rau, Rob Selina, C. G. De Pree

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

Building on the remarkable success of the VLA, VLBA, and ALMA, NRAO is planning a large collecting area interferometer which will replace the VLA and VLBA -the next generation Very Large Array (ngVLA) [1].For many of the same reasons that the VLA was constructed where it was (fraction of sky visible, quality of site, accessibility, etc.), the core and bulk of the collecting area of the ngVLA will be on the Plains of San Agustin, near where the center of the current VLA is.Despite the general quality of the site, RFI is nearly ubiquitous, especially at frequencies below 2 GHz, and the spectrum is getting worse with time, as experienced in other areas across the world.Also, because of the spatial resolution needed to satisfy science requirements, antennas will be spread across the U.S., Canada, and northern Mexico, and so be in various different RFI environments (see Figure 1 for locations of antennas in the current design) [2,3].

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)0.002
metaresearch head score (Gemma)0.002
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: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.005

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 head0.065
GPT teacher head0.258
Teacher spread0.194 · 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
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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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