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Record W1199960 · doi:10.22323/1.014.0057

Numerical influences on galaxy formation

2004· article· en· W1199960 on OpenAlexaff
Tobias Kaufmann, Lucio Mayer, Ben Moore, Joachim Stadel, James Wadsley

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicScientific Research and Discoveries
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPhysicsBaryonDark matterGalaxyCuspy halo problemAstrophysicsHaloDark matter haloBar (unit)Angular momentumGalaxy formation and evolutionDynamical frictionClassical mechanics

Abstract

fetched live from OpenAlex

We perform N-Body/SPH simulations of disk galaxy formation inside equilibrium spherical and triaxial cuspy dark matter halos.We systematically study the disk properties and morphology as we increase the numbers of dark matter and gas particles from 10 4 to 10 6 and change the force resolution.The force resolution influences the morphological evolution of the disk quite dramatically.Unless the baryon fraction is significantly lower than the universal value, with high force resolution a gaseous bar always forms within a billion years after allowing cooling to begin.The bar interacts with the disk, transferring angular momentum and increasing its scale length.In none of the simulations does the final mass distribution of the baryons obey a single exponential profile.Indeed within a few hundred parsecs to a kiloparsec from the center the density rises much more steeply than in the rest of the disk, and this is true irrespective of the presence of the bar.

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)0.001
metaresearch head score (Gemma)0.020
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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
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 head0.017
GPT teacher head0.293
Teacher spread0.276 · 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

Citations0
Published2004
Admission routes1
Has abstractyes

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