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Record W4403550648 · doi:10.1051/epjconf/202430601004

Macroscopic-Microscopic Fission Yields

2024· article· en· W4403550648 on OpenAlexaff
Matthew Ryan Mumpower, T. M. Sprouse, Marc Verrière, Nicole Vassh, J. Randrup

Bibliographic record

VenueEPJ Web of Conferences · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear physics research studies
Canadian institutionsTRIUMF
FundersLos Alamos National LaboratoryNational Nuclear Security AdministrationU.S. Department of Energy
KeywordsFissionNuclear physicsPhysicsNuclear fissionStatistical physicsNeutron

Abstract

fetched live from OpenAlex

We utilize the macroscopic-microscopic approach to fission to calculate nascent fragment distributions. Assuming strongly damped shape motion, we run many iterations of a Metropolis random walk across nuclear potential-energy surfaces to obtain sufficient scission statistics. Our nuclear potential surfaces consist of a macroscopic energy from the Finite-Range Liquid-Drop Model (FRLDM) and microscopic terms that arise from the single-particle spectra. We compare our predictions for two major actinides to experimental data. We present global trends from this modeling that manifest as a function of mass number of the fissioning species. We discuss the impact of fission yield modeling on nucleosynthetic outcomes.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.555
Threshold uncertainty score0.998

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.324
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.

Study designTheoretical or conceptual
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
Published2024
Admission routes1
Has abstractyes

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