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Record W2891994652 · doi:10.3847/1538-4357/aae538

Chemodynamics of Dwarf Galaxies under Ram Pressure

2018· article· en· W2891994652 on OpenAlexafffund

Bibliographic record

VenueThe Astrophysical Journal · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsUniversité LavalCentre for Research in Astrophysics of Québec
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Southampton
KeywordsRam pressureDwarf galaxyGalaxyDwarf galaxy problemHaloDwarf spheroidal galaxyTidal forceDark matter

Abstract

fetched live from OpenAlex

Abstract By implementing a dynamic wind tunnel model in a smoothed particle chemodynamic/hydrodynamic simulation suite, we have investigated the effects of ram pressure and tidal forces on dwarf galaxies similar to the Magellanic Clouds, within host galaxies with gas and dark matter halos that are varied, to compare the relative effects of tides and ram pressure. We concentrate on how the distributions of metals are affected by interactions. We find that while ram pressure and tidal forces have some effect on dwarf galaxy outflows, these effects do not produce large differences in the metal distributions of the dwarf disks, other than truncation in the outer regions in some cases, and that confinement from the host galaxy gas halo appears to be more significant than ram pressure stripping. We find that stochastic variations in the star formation rate can explain the remaining variations in disk metal properties. This raises questions on the cause of low metallicities in dwarf galaxies.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.217
Teacher spread0.210 · 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 designSimulation or modeling
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

Citations9
Published2018
Admission routes2
Has abstractyes

Explore more

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