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Preface

2020· article· en· W4253372153 on OpenAlexaboutno aff

Bibliographic record

VenueJournal of Physics Conference Series · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicHigh-Energy Particle Collisions Research
Canadian institutionsnot available
Fundersnot available
KeywordsNuclear physicsLarge Hadron ColliderPhysics

Abstract

fetched live from OpenAlex

These are the proceedings of the 36th edition of the Winter Workshop on Nuclear Dynamics, which was held In Puerto Vallarta, Mexico, in March 2020. Our meeting was the final in-person gathering in our field before the international lockdown period due to Covid-19. As in previous years the unique character of this conference series has allowed us to bring scientists from various fields of nuclear physics together to discuss their scientific achievements. At the high-energy frontier very exciting results not only from the heavy-ion collisions but also smaller systems at the LHC were shown. At this time the LHC experiments are upgrading their detectors for the third large run period, which is scheduled to start in early 2022. Several talks focused on the future experimental program at the LHC. At the lower energies the second RHIC beam energy scan is underway to span the gap between the SPS and RHIC and search for critical phenomena in the nuclear matter phase diagram. This program is complemented by spin and small systems measurements at the highest RHIC energies. The future programs of sPHENIX and the planned electron-ion collider (EIC) were represented as well. Theory progress has been made across all these different energy and system size regimes, and the new data, in particular from the LHC and RHIC, are motivating more detailed modeling and a deeper understanding of the underlying physics. These proceedings of the 36th Winter Workshop on Nuclear Dynamics again provide a snapshot of the status of the field. The articles, many of which are written by some of the most promising young scientists in the field, are conveying the present excitement that permeates all subfields of nuclear science. All papers published in this volume have been peer reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. Rene Bellwied (University of Houston, USA) John Harris (Yale University, USA) Claudia Ratti (University of Houston, USA) Anthony Timmins (University of Houston, USA)

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.854
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.044
GPT teacher head0.289
Teacher spread0.245 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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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Citations0
Published2020
Admission routes1
Has abstractyes

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