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Unveiling nuclear isomers through multiple-reflection time-of-flight mass spectrometry

2024· article· en· W4394808505 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 Special Topics · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear physics research studies
Canadian institutionsTRIUMF
FundersGSI Helmholtzzentrum für SchwerionenforschungDeutsche Forschungsgemeinschaft
KeywordsMass spectrometryTime of flightCharacterization (materials science)Resolution (logic)Time-of-flight mass spectrometryNuclear physicsChemistryComputer sciencePhysicsMaterials scienceNanotechnologyChromatographyArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Nuclear isomers, the excited meta-stable states of nuclei, offer profound insights into nuclear structure. This article reviews the intersection of nuclear isomer research with mass spectrometry methodologies, particularly focusing on novel capabilities of the multiple-reflection time-of-flight mass spectrometry (MR-TOF-MS) technique. Through a comprehensive examination of established methods for isomer identification and characterization, alongside the technical principles underlying MR-TOF-MS, this review discusses the pivotal role of mass spectrometry in advancing our understanding of nuclear isomers. The operational principles and recent developments in MR-TOF-MS technology are explained and exemplified through case studies from prominent research facilities. Furthermore, this work discusses ongoing efforts to enhance sensitivity, resolution, and measurement capabilities in MR-TOF-MS, promising continued advancements in nuclear physics research and applications.

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)0.000
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: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.940
Threshold uncertainty score1.000

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.022
GPT teacher head0.294
Teacher spread0.272 · 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