Neutron and Gamma-ray Detection using a Cs<sub>2</sub>LiYCl<sub>6</sub>Scintillator
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".