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Record W2006597046 · doi:10.1142/s0218301311019921

MONTE-CARLO SIMULATION OF HEAVY-ION COLLISIONS

2011· article· en· W2006597046 on OpenAlexaffabout
Björn Schenke, Sangyong Jeon, Charles Gale

Bibliographic record

VenueInternational Journal of Modern Physics E · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicHigh-Energy Particle Collisions Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsPhysicsEvent (particle physics)Monte Carlo methodModular designHeavy ionEvent generatorNuclear physicsGenerator (circuit theory)PartonNucleusIonComputational physicsStatistical physicsComputer scienceHadronAstrophysicsPower (physics)Quantum mechanics

Abstract

fetched live from OpenAlex

Results from the Modular Algorithm for Relativistic Treatment of heavy IoN Interactions (MARTINI) are presented. This comprehensive event generator for the hard and penetrating probes in high energy nucleus-nucleus collisions employs a time evolution model for the soft background, PYTHIA 8.1 and the McGill-AMY parton evolution scheme including radiative as well as elastic processes. It generates full event configurations in the high pT region, allowing to perform the same processing as with experimental data, such as multi-particle correlation analyses and full jet reconstruction.

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)0.002
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: none
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.055
GPT teacher head0.330
Teacher spread0.275 · 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

Citations1
Published2011
Admission routes2
Has abstractyes

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