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Record W2512114797 · doi:10.1139/cjp-2016-0330

Investigations of gamma ray and fast neutron shielding properties of tellurite glasses with different oxide compositions

2016· article· en· W2512114797 on OpenAlexvenueno aff
M.I. Sayyed

Bibliographic record

VenueCanadian Journal of Physics · 2016
Typearticle
Languageen
FieldMaterials Science
TopicRadiation Shielding Materials Analysis
Canadian institutionsnot available
FundersUniversity of Tabuk
KeywordsMass attenuation coefficientElectromagnetic shieldingPhysicsAttenuationNeutronEffective atomic numberPhotonGamma rayRange (aeronautics)ElectronRadiationNeutron temperatureNeutron radiationPhoton energyAnalytical Chemistry (journal)OxideAttenuation coefficientAtomic numberAtomic physicsNuclear physicsOpticsMaterials scienceComposite materialChemistry

Abstract

fetched live from OpenAlex

In the present paper, the basic radiation parameters of tellurite glasses with different forming oxides (B2O3, BaO, K2O, V2O5, WO3, and ZnO) have been studied over a wide photon energy range from 1 keV to 100 GeV, using WinXCom program. These parameters are the mass attenuation coefficient (μ/ρ), effective atomic number (Zeff), and electron density (Ne,eff). In addition, the macroscopic effective removal cross sections (ΣR) for fast neutrons have been calculated. The dependence of different parameters on incident photon energy and chemical content has been discussed. Among the selected glass systems TeO2–WO3 and TeO2–B2O3 showed superior shielding properties for gamma-ray and neutrons, respectively. It is shown that the selected glass systems have higher values of the mass attenuation coefficients (μ/ρ) than concretes and 0.7SiO2:0.3BaO glass. These results indicate that tellurite glasses can be used as radiation shielding materials.

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)0.000
metaresearch head score (Gemma)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0000.000

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 head0.019
GPT teacher head0.201
Teacher spread0.181 · 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

Citations102
Published2016
Admission routes1
Has abstractyes

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