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PUMA, antiProton unstable matter annihilation

2022· article· en· W3213391075 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe European Physical Journal A · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear physics research studies
Canadian institutionsTRIUMF
FundersNuclear PhysicsH2020 European Research CouncilInstitut National de Physique Nucléaire et de Physique des ParticulesRIKENTechnische Universität DarmstadtCentre National de la Recherche ScientifiqueCERNAlexander von Humboldt-Stiftung
KeywordsAntiprotonPhysicsNuclear physicsAnnihilationLarge Hadron ColliderColliderNeutronParticle physicsProton

Abstract

fetched live from OpenAlex

Abstract PUMA, antiProton Unstable Matter Annihilation, is a nuclear-physics experiment at CERN aiming at probing the surface properties of stable and rare isotopes by use of low-energy antiprotons. Low-energy antiprotons offer a very unique sensitivity to the neutron and proton densities at the annihilation site, i.e. in the tail of the nuclear density. Today, no facility provides a collider of low-energy radioactive ions and low-energy antiprotons: while not being a collider experiment, PUMA aims at transporting one billion antiprotons from ELENA, the Extra-Low-ENergy Antiproton ring, to ISOLDE, the rare-isotope beam facility of CERN. PUMA will enable the capture of low-energy antiprotons by short-lived nuclei and the measurement of the emitted radiations. In this way, PUMA will give access to the so-far largely unexplored isospin composition of the nuclear-radial-density tail of radioactive nuclei. The motivations, concept and current status of the PUMA experiment are presented.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.604
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.015
GPT teacher head0.267
Teacher spread0.251 · 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