Simplified Approach for Preliminary Evaluation of Effective Dose Rates for Field Applications of D-T Neutron Generators
Bibliographic record
Abstract
Portable neutron generators are one of the most widespread sources of fast neutrons for different applications, from activation analysis to quantitative estimation of fissile and fertile materials in radioactive waste packages. New techniques based on these devices have been developed for homeland security, e.g. neutron interrogations of packages suspected of containing substances such as explosives. When using portable generators determination of radiation protection quantities is affected by a series of boundary conditions that could differ from an experimental set-up to another, and Radiation Protection Experts cannot assess operator exposure without the aid of time-expensive Monte Carlo simulations. As it is impossible to foresee all kind of scenarios involving such applications, and time is often a critical variable, safety assessment requires faster, even if less accurate, tools for exposure evaluation. In this article experimental measurements of dose rates in a real scenario involving a D-T neutron generator are considered, to validate the Monte Carlo model developed. Consequently, it was possible to demonstrate that the calibration factor at 14 MeV for a neutron dosimeter provides conservative dose estimates and to propose a stand-alone, fast, and easy-to-use calculation tool which is usable on field by the Radiation Protection Expert without full Monte Carlo calculations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".