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Record W2416509963 · doi:10.1142/s0218301316300058

Predictions for p+Pb Collisions at sNN = 5TeV: Comparison with Data

2016· article· en· W2416509963 on OpenAlexaff
Javier L. Albacete, François Arleo, G. G. Barnaföldi, J. Barrette, Wei-Tian Deng, Adrian Dumitru, K. Eskola, E. G. Ferreiro, Frédéric Fleuret, H. Fujii, Miklós Gyulassy, Szilveszter Harangozó, Ilkka Helenius, Zhong-Bo Kang, Piotr Kotko, Krzysztof Kutak, Jean-Philippe Lansberg, P. Lévai, Zi-Wei Lin, Yasushi Nara, A. Rakotozafindrabe, Gábor Papp, Hannu Paukkunen, Stéphane Peigné, M. Petrovici, Jian-Wei Qiu, Amir H. Rezaeian, Peng Ru, Sebastian Sapeta, Vasile Pop, Ivan Vitev, R. Vogt, Enke Wang, Xin-Nian Wang, Hongxi Xing, Rong Xu, Ben-Wei Zhang, Wei-Ning Zhang

Bibliographic record

VenueInternational Journal of Modern Physics E · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicHigh-Energy Particle Collisions Research
Canadian institutionsMcGill University
FundersLawrence Livermore National LaboratoryNuclear PhysicsCentre National de la Recherche ScientifiqueNational Natural Science Foundation of ChinaOffice of ScienceAgence Nationale de la RechercheNarodowe Centrum NaukiU.S. Department of Energy
KeywordsLarge Hadron ColliderPhysicsParticle physicsNuclear physicsMathematicsCombinatorics

Abstract

fetched live from OpenAlex

Predictions made in Albacete et al. [Int. J. Mod. Phys. E 22 (2013) 1330007] prior to the LHC [Formula: see text]Pb run at [Formula: see text] TeV are compared to currently available data. Some predictions shown here have been updated by including the same experimental cuts as the data. Some additional predictions are also presented, especially for quarkonia, that were provided to the experiments before the data were made public but were too late for the original publication.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0040.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0080.004

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 head0.066
GPT teacher head0.364
Teacher spread0.298 · 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".

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Citations35
Published2016
Admission routes1
Has abstractyes

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Same venueInternational Journal of Modern Physics ESame topicHigh-Energy Particle Collisions ResearchFrench-language works237,207