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Record W1996298852 · doi:10.1142/s0218301314500013

Effects of beam energy distribution on fusion-evaporation excitation functions

2014· article· en· W1996298852 on OpenAlexfundno aff
R. N. Sagaidak, A. N. Andreyev

Bibliographic record

VenueInternational Journal of Modern Physics E · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear physics research studies
Canadian institutionsnot available
FundersJapan Atomic Energy AgencyScience and Technology Facilities CouncilRussian Foundation for Basic ResearchMcMaster University
KeywordsExcitationEvaporationFusionMonte Carlo methodPhysicsBeam (structure)Atomic physicsComputational physicsEnergy (signal processing)OpticsMathematicsQuantum mechanicsStatisticsThermodynamics

Abstract

fetched live from OpenAlex

A numerical approach has been developed for the calculation of fusion-evaporation excitation functions taking into account the heavy ion beam energy distribution inside the target. The approach uses statistical model approximations of the HIVAP code for the calculations of evaporation residue excitation functions in the ordinary way and the Monte Carlo TRIM simulations for the determination of beam energy distributions inside the target. Several applications of the approach have been considered to evaluate the effect of the beam energy distributions inside the target on fusion-evaporation excitation functions calculated in the ordinary way.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.266
Teacher spread0.257 · 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 designBench or experimental
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

Citations5
Published2014
Admission routes1
Has abstractyes

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