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Record W4221166260 · doi:10.1016/j.ppnp.2022.103962

Transport model comparison studies of intermediate-energy heavy-ion collisions

2022· article· en· W4221166260 on OpenAlexafffund
H.H. Wolter, M. Colonna, Dan Cozma, Paweł Danielewicz, Che Ming Ko, Akira Ono, M. B. Tsang, Yingxun Zhang, Elena Bratkovskaya, T. Gaitanos, A. Le Fèvre, Natsumi Ikeno, Young‐Man Kim, S. Mallik, P. Napolitani, Dmytro Oliinychenko, Tatsuhiko Ogawa, Yongjia Wang, Janus Weil, Fengshou Zhang, 张震, W. Cassing, Lie-Wen Chen, Hui-Gan Cheng, Hannah Elfner, C. Hartnack, Shintaro Hashimoto, Sangyong Jeon, Kyungil Kim, Baoan Li, Chang‐Hwan Lee, Zhuxia Li, U. Mosel, Yasushi Nara, Koji Niita, Akira Ohnishi, Tatsuhiko Sato, Agnieszka Sorensen

Bibliographic record

VenueProgress in Particle and Nuclear Physics · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicHigh-Energy Particle Collisions Research
Canadian institutionsMcGill University
FundersJapan Society for the Promotion of ScienceCollege of Engineering, Michigan State UniversityOffice of ScienceNuclear PhysicsNatural Sciences and Engineering Research Council of CanadaMinistry of Science and ICT, South KoreaChina Institute of Atomic EnergyTsinghua UniversityInstitute for Basic ScienceNational Science FoundationShanghai Jiao Tong UniversityMinistry of Science, ICT and Future PlanningNational Natural Science Foundation of ChinaNational Key Research and Development Program of ChinaMichigan State UniversityNational Research FoundationDeutsche ForschungsgemeinschaftNational Research Foundation of KoreaU.S. Department of Energy
KeywordsHeavy ionPhysicsIonCode (set theory)Statistical physicsNuclear physicsComputer scienceSet (abstract data type)Quantum mechanics

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.001
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 score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.045
GPT teacher head0.341
Teacher spread0.295 · 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
Published2022
Admission routes2
Has abstractno

Explore more

Same venueProgress in Particle and Nuclear PhysicsSame topicHigh-Energy Particle Collisions ResearchFrench-language works237,207