Design of a Tritium-In-Air Monitor Using Field-Programmable Gate Arrays
Bibliographic record
Abstract
Field-programmable gate arrays (FPGAs) have recently garnered significant interest for certain applications within the nuclear field including instrumentation and control (I&C) systems, pulse measurement systems, particle detectors, and health physics. In CANada Deuterium Uranium (CANDU) nuclear power plants, the use of heavy water (D2O) as the moderator leads to increased production of tritium, which poses a health risk and must be monitored by tritium-in-air monitors (TAMs). Traditional TAMs are mostly designed using microprocessors. More recent studies show that FPGAs could be a potential alternative to implement the electronic logic used in radiation detectors, such as the TAM, more effectively. In this paper, an FPGA-based TAM is designed and constructed in a laboratory setting using an FPGA-based cRIO system. New functionalities, such as the detection of carbon-14 and the addition of noble-gas compensation, are incorporated into a new FPGA-based TAM along with the standard functions included in the original microprocessor-based TAM. The effectiveness of the new design is demonstrated through simulations as well as laboratory testing on the prototype system. Potential issues caused by radiation interactions with the FPGA are beyond the scope of this work.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".