Radiation-Induced Damage–Based System and Method for Indirectly Monitoring High-Dose Ionizing Radiation
Bibliographic record
Abstract
Proximate to nuclear power plant severe accidents, sustained high levels of gamma radiative flux are perilous not only to human health but also to the functionality of conventional radiation-monitoring devices. Effective accident mitigation presents a significant challenge because the gamma radiation adversely affects the means by which it is measured. Deployments of large numbers of radiation-hardened monitoring devices, required to meet the demands of adequate system reliability and the large spatiotemporal scales associated with such accidents, are expected to be prohibitively expensive. As an affordable alternative, this paper proposes usage of a wireless sensor network (WSN) built with unshielded low-cost integrated circuits (ICs) acting as consumable proportional sensors of gamma radiation dose. Adverse responses of ICs to damaging gamma radiation dose can be characterized statistically, in controlled laboratory experiments. In subsequent field application, responses of individual ICs, transmitted over a WSN to a remote computer, can be translated into local dose measurements using correlations obtained via the laboratory characterization. Experiments to characterize adverse response to radiation dose were performed on multiple complementary metal-oxide-semiconductor–based electrically erasable programmable read-only memory devices in a Gammacell 220 Cobalt-60 Irradiation Unit (60Co source) at the Canadian Nuclear Laboratories. Details of the experiments, data analyses, and a small-scale prototype WSN are discussed in this paper. Outcomes of the experiments a nd analysis support the concept of using low-cost consumable ICs in a WSN to measure high levels of gamma radiation dose associated with nuclear power plant severe accidents.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".