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Record W2047894542 · doi:10.1088/2041-8205/763/2/l41

DWARF GALAXIES AND THE COSMIC WEB

2013· article· en· W2047894542 on OpenAlexaff
Alejandro Benítez-Llambay, Julio F. Navarro, M. G. Abadi, Stefan Gottlöber, Gustavo Yepes, Yehuda Hoffman, Matthias Steinmetz

Bibliographic record

VenueThe Astrophysical Journal Letters · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsUniversity of Victoria
FundersLeibniz-GemeinschaftLeibniz-RechenzentrumBarcelona Supercomputing CenterIsrael Science Foundation
KeywordsPhysicsAstrophysicsDwarf galaxy problemGalaxyReionizationGalaxy formation and evolutionDwarf galaxyCuspy halo problemStar formationHaloDark matterDark matter haloAstronomyRedshift

Abstract

fetched live from OpenAlex

We use a cosmological simulation of the formation of the Local Group of Galaxies to identify a mechanism that enables the removal of baryons from low-mass halos without appealing to feedback or reionization. As the Local Group forms, matter bound to it develops a network of filaments and pancakes. This moving web of gas and dark matter drifts and sweeps a large volume, overtaking many halos in the process. The dark matter content of these halos is unaffected but their gas can be efficiently removed by ram pressure. The loss of gas is especially pronounced in low-mass halos due to their lower binding energy and has a dramatic effect on the star formation history of affected systems. This "cosmic web stripping" may help to explain the scarcity of dwarf galaxies compared with the numerous low-mass halos expected in ΛCDM and the large diversity of star formation histories and morphologies characteristic of faint galaxies. Although our results are based on a single high-resolution simulation, it is likely that the hydrodynamical interaction of dwarf galaxies with the cosmic web is a crucial ingredient so far missing from galaxy formation models.

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)0.002
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: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.005
GPT teacher head0.184
Teacher spread0.179 · 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

Citations133
Published2013
Admission routes1
Has abstractyes

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