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Record W6931234603 · doi:10.5281/zenodo.3254756

New perspectives on Galactic Faraday tomography at low frequencies

2019· article· en· W6931234603 on OpenAlexaff

Bibliographic record

VenueFigshare · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadio Astronomy Observations and Technology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLOFARPolarization (electrochemistry)SkyFaraday effectFaraday cageMagnetic fieldAngular resolution (graph drawing)

Abstract

fetched live from OpenAlex

Talk given at "New Perspectives on Galactic Magnetism" conference, 11 June 2019. Abstract: The new generation of low-frequency (sub-GHz) radio telescopes, particularly LOFAR and MWA, have opened up the polarized sky in new and amazing ways. We now have the capability to explore low frequency polarization at high resolution, on large angular scales, and with large fields of view, all at the same time. In my talk I will review low-frequency Faraday tomography to date, and discuss how we can take advantage of the properties of polarization at low-frequencies to inform our analysis of the observations. In some cases, we can break the degeneracies that prevent us from modelling the magnetic field along the line of sight. There are several ongoing, planned, and possible surveys that will let us break open the low frequency polarized sky.

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)0.004
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.003
Scholarly communication0.0040.009
Open science0.0010.002
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0080.003

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 head0.012
GPT teacher head0.213
Teacher spread0.201 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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