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R-matrix atomic data for application in astrophysics and fusion

2023· article· en· W4381989543 on OpenAlexfundno aff
C A Ramsbottom, C P Ballance, M. McCann

Bibliographic record

VenueThe European Physical Journal D · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear physics research studies
Canadian institutionsnot available
FundersCentral Laser Facility, Science and Technology Facilities CouncilScience and Technology Facilities CouncilQueen's University BelfastQueen's UniversityAuburn University
KeywordsPhysicsExcitationCollisionCollisional excitationAtomic physicsMatrix (chemical analysis)Nuclear fusionIonSpectral lineRadiative transferBinary numberR-matrixAstrophysicsMaterials scienceComputer scienceAstronomy

Abstract

fetched live from OpenAlex

Abstract A selection of electron-impact excitation R -matrix calculations completed by the collision group at Queens University Belfast are discussed. The atomic data calculated are the foundation of applications in a variety of fields, a sample of which are presented here. They include an analysis of Fe ii emission in active galactic nuclei, the possible presence of Pt and Au in the spectra of binary neutron star mergers and the viability of W ions as a diagnostic tool in magnetically confined tokamak plasmas. The radiative atomic data were computed using the most recent version of the fully relativistic structure code grasp0 and the collision cross sections were evaluated using the recently developed pdarc R -matrix package. Graphic abstract

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.923
Threshold uncertainty score0.303

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.034
GPT teacher head0.334
Teacher spread0.300 · 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 designOther design
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

Citations2
Published2023
Admission routes1
Has abstractyes

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