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Estimation of fusion-evaporation cross sections by artificial neural networks

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Serkan Akkoyun

Nuclear Experiment · Submitted 1 Jul 2019

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim The results of this study indicate that ANN method is capable for the estimation of cross section values of fusion-evaporation reactions.

how it is stated · hedged possibility 0.83 read the record →

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arXiv ID1907.00579 (version 1)
Pith IDTHEMSKUD, Pith's permanent record of this paper and its review
CitationSerkan Akkoyun. "Estimation of fusion-evaporation cross sections by artificial neural networks." arXiv:1907.00579 (2019). https://pith.science/paper/THEMSKUD
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,645 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.21 · human participants 0.01 · controlled comparison 0.19 · randomized assignment 0.01 · states quantitative result 0.99 · compares with prior results 0.89 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Accurate determination of fusion-evaporation reaction cross section is important in experimental nuclear physics studies. In this study, by using artificial neural network (ANN) method, we have estimated the cross section values for different reactions. The related root mean square errors have been obtained as 18.5 and 110.4mb for training and test data which correspond to 1.8% and 10.5% deviations from the experimental values, respectively.This order of deviations is lower than the cross section value from most common theoretical calculations. The results of this study indicate that ANN method is capable for the estimation of cross section values of fusion-evaporation reactions.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Accurate determination of fusion-evaporation reaction cross section is important in experimental nuclear physics studies. background 1.00 0.05 0.02 asserted 1.00
s1 In this study, by using artificial neural network (ANN) method, we have estimated the cross section values for different reactions. contribution 0.60 0.95 0.05 asserted 1.00
s2 The related root mean square errors have been obtained as 18.5 and 110.4mb for training and test data which correspond to 1.8% and 10.5% deviations from the experimental values, respectively.This order of deviations is lower than the cross section value from most common theoretical calculations. result 0.98 0.95 0.13 asserted 1.00
s3 The results of this study indicate that ANN method is capable for the estimation of cross section values of fusion-evaporation reactions. interpretation 0.98 0.90 0.95 hedged possibility 0.83

What kind of paper the classifier reports this is: method or tool 0.59 · evidence from simulation or computation 0.75.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
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  • P abstract
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  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Accurate determination of fusion-evaporation reaction cross section is important in experimental nuclear physics studies. In this study, by using artificial neural network (ANN) method, we have estimated the cross section values for different reactions. The related root mean square errors have been obtained as 18.5 and 110.4mb for training and test data which correspond to 1.8% and 10.5% deviations from the experimental values, respectively.This order of deviations is lower than the cross section value from most common theoretical calculations. The results of this study indicate that ANN method is capable for the estimation of cross section values of fusion-evaporation reactions.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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