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Record W4416595382 · doi:10.18727/0722-6691/5393

Multiphase Astrophysics to Unveil the Virgo Environment (MAUVE)

2025· article· W4416595382 on OpenAlexaff
Barbara Catinella, L. Cortese, Jiayi Sun, Toby Brown, Éric Emsellem, Amelia Fraser-McKelvie, Adam B. Watts, Amy Attwater, Andrew Battisti, Alessandro Boselli, Woorak Choi, Aeree Chung, Elisabete da Cunha, Timothy A. Davis, Sara L. Ellison, Pavel Jáchym, María J. Jiménez-Donaire, Tutku Kolcu, Bumhyun Lee, James McGregor, Ian Roberts, Eva Schinnerer, Kristine Spekkens, Sabine Thater, D. Thilker, Jesse van de Sande, Vicente Villanueva, Thomas G. Williams, Nikki Zabel

Bibliographic record

VenueArXiv.org · 2025
Typearticle
Language
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsQueen's UniversityUniversity of VictoriaUniversity of WaterlooMcMaster UniversityHerzberg Institute of Astrophysics
Fundersnot available
KeywordsVirgo ClusterGalaxyStar formationCluster (spacecraft)Galaxy clusterHigh resolution

Abstract

fetched live from OpenAlex

The Multiphase Astrophysics to Unveil the Virgo Environment (MAUVE) project is a multi-facility programme exploring how dense environments transform galaxies. Combining a VLT/MUSE P110 Large Programme and ALMA observations of 40 late- type Virgo Cluster galaxies, MAUVE resolves star formation, kinematics, and chemical enrichment within their molecular gas discs. A key goal is to track the evolution of cold gas that survives in the inner regions of satellites after entering the cluster, and how it evolves across different infall stages. With its high spatial resolution — probing down to the physical scales of giant molecular cloud complexes — and multiphase synergy, MAUVE aims to offer a time-resolved view of environmental quenching and set a new benchmark for cluster galaxy studies.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.717
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.003

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 head0.015
GPT teacher head0.246
Teacher spread0.231 · 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 teacher head, not a consensus.

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".

Quick stats

Citations4
Published2025
Admission routes1
Has abstractyes

Explore more

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