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Record W1982810192 · doi:10.1051/epjconf/20146611018

Neutron and Gamma-ray Detection using a Cs<sub>2</sub>LiYCl<sub>6</sub>Scintillator

2014· article· en· W1982810192 on OpenAlexaff
Nafisah Khan, R. Machrafi

Bibliographic record

VenueEPJ Web of Conferences · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadiation Detection and Scintillator Technologies
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsScintillatorNeutronNeutron detectionMonte Carlo methodPhysicsNuclear engineeringNuclear physicsNeutron temperatureGamma rayRadiationComputational physicsDetectorOpticsEngineering

Abstract

fetched live from OpenAlex

A new scintillator, Cs2LiYCl6 (CLYC), has recently gained interest due to its dual capability to detect neutron and gamma radiation. In addition to its high resolution to detect gamma-rays, this sensor can serve in detecting both thermal and fast neutrons through 6Li(n,α) and 35Cl(n,p) reactions, respectively. For fast neutron detection, the current sensor technology has challenges and drawbacks, such as detection efficiency and energy dependence. In this regard, due to the presence of the 35Cl isotope, CLYC can overcome those challenges. The response functions of this scintillator to neutron and gamma radiation has been obtained using Monte Carlo N-Particle eXtended code (MCNPX). The simulation results and the sensor’s applicability to neutron spectrometry and dosimetry has been discussed and analyzed.

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 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 score0.157
Threshold uncertainty score0.902

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)0.0000.000

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 head0.012
GPT teacher head0.224
Teacher spread0.211 · 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.

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

Citations4
Published2014
Admission routes1
Has abstractyes

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