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Record W2296208391 · doi:10.1109/hpcs.2008.38

Computational Astrophysics

2008· article· en· W2296208391 on OpenAlexaff
Hugh Couchman

Bibliographic record

VenueProceedings/Proceedings (International Symposium on High Performance Computing Systems and Applications) · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstronomy and Astrophysical Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCosmologyComputational astrophysicsPhysicsCOSMIC cancer databaseGalaxyAstrophysicsGravitationAstronomy

Abstract

fetched live from OpenAlex

Summary form only given. Large-scale computation has achieved a pivotal role in many areas of astrophysics and cosmology. Not only does increasing computational power allow us to better model the huge range of spatial and temporal scales in the universe, but simulations provide in many cases the only laboratory in which we may actively experiment on cosmic phenomena. The talk will highlight some of the significant achievements in numerical astrophysics and cosmology over the past decade and describe some of the key challenges that we must face in the future. It will focus, in particular, on some recent state-of-the-art models of dwarf galaxies and illustrate the subtle interplay between gravitational and astrophysical processes in these systems.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.229
Threshold uncertainty score0.765

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0060.003
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.2290.129

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 head0.010
GPT teacher head0.243
Teacher spread0.233 · 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 designTheoretical or conceptual
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

Citations25
Published2008
Admission routes1
Has abstractyes

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