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Record W2904159842 · doi:10.3390/galaxies6040142

Magnetic Fields in Galaxy Clusters and in the Large-Scale Structure of the Universe

2018· article· en· W2904159842 on OpenAlexaff
V. Vacca, M. Murgia, F. Govoni, T. A. Enßlin, Niels Oppermann, L. Feretti, G. Giovannini, F. Loi

Bibliographic record

VenueGalaxies · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsCanadian Institute for Theoretical AstrophysicsUniversity of Toronto
FundersJet Propulsion LaboratoryRegione Autonoma della SardegnaMinistero dell’Istruzione, dell’Università e della RicercaCalifornia Institute of TechnologyNational Aeronautics and Space AdministrationNational Radio Astronomy ObservatoryIstituto Nazionale di AstrofisicaNational Science Foundation
KeywordsPhysicsAstrophysicsCOSMIC cancer databaseGalaxyMagnetismUniverseGalaxy clusterAstronomyCondensed matter physics

Abstract

fetched live from OpenAlex

The formation and history of cosmic magnetism is still widely unknown. Significant progress can be made through the study of magnetic fields properties in the large-scale structure of the Universe: galaxy clusters, filaments, and voids of the cosmic web. A powerful tool to study magnetization of these environments is represented by radio observations of diffuse synchrotron sources and background or embedded radio galaxies. To draw a detailed picture of cosmic magnetism, high-quality data of these sources need to be used in conjunction with sophisticated tools of analysis.

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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.004
GPT teacher head0.190
Teacher spread0.186 · 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

Citations30
Published2018
Admission routes1
Has abstractyes

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