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Microscopic cluster model of<i>α</i>+<i>α</i>bremsstrahlung following a Siegert approach

2014· article· en· W2030896663 on OpenAlexafffund
Jérémy Dohet-Eraly, D. Baye

Bibliographic record

VenueJournal of Physics Conference Series · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear physics research studies
Canadian institutionsTRIUMF
FundersNational Research Council CanadaTRIUMF
KeywordsBremsstrahlungPhysicsNuclear physicsPhotonCluster (spacecraft)Extension (predicate logic)Experimental dataStatistical physicsParticle physicsQuantum mechanicsMathematicsComputer scienceElectronStatistics

Abstract

fetched live from OpenAlex

We have recently developed a microscopic cluster model of nucleus-nucleus bremsstrahlung, which takes implicitly account of a part of the meson-exchange current effects, by applying an extension of the Siegert theorem valid for arbitrary photon energy. This model is applied to the α + α bremsstrahlung. The differences between the bremsstrahlung cross sections obtained in the non-Siegert and Siegert approaches are displayed and discussed. The theoretical bremsstrahlung cross sections are compared with experimental measurements. Whereas the agreement with quite old experimental data is fair, a rather important discrepancy between the theoretical results and the more recent experimental data is noticed. Some paths to investigate this disagreement are proposed.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0040.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.027
GPT teacher head0.272
Teacher spread0.246 · 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".

Quick stats

Citations0
Published2014
Admission routes2
Has abstractyes

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