Bibliographic record
Abstract
국내 원전에서는 과거에 불균일 방사선장이 형성되는 고피폭 방사선작업에 2개의 복수선량계(TLD)를 머리와 기슴에 패용하였으며, 이들 선량계 판독값 중에서 최대값을 유효선량으로 평가함으로써 일정 부분이 과대평가되고 있는 것으로 나타났다. 따라서 이러한 문제점을 개선하고자 국제적인 기관에서 제시된 복수선량계 알고리즘을 대상으로 적절한 알고리즘을 선정하기 위한 현장적용 시험을 실시하였다. 여기에는 캐나다 원전사업자(OPG), 미국표준기술협회(ANSI HPS N13.41), NCRP(55/50), NCRP(70/30), EPRI (NRC), Lakshmanan, Kim(Texas AM one on the chest and the other on the head. In this way, the effective dose for radiation workers at NPPs was determined by the high deep dose between two radiation dose from these TLDs. This represented a conservative method of evaluating the degree of exposure to radiation. In this study, to prevent the overestimation of the effective dose, field application experiments were implemented using two-dosimeter algorithms developed by several international institutes for the selection of an optimal algorithm. The algorithms used by the Canadian Ontario Power Generation (OPG) and American ANSI HPS N13.41, NCRP (55/50), NCRP (70/30), EPRI (NRC), Lakslumanan, and Kim (Texas A&M University) were extensively analyzed as two-dosimeter algorithms. In particular, three additional TLDs were provided to radiation workers who wore them on the head, chest, and back during maintenance periods, and the measured value were analyzed. The results found no significant differences among the calculated effective doses, apart from Lakshmanan's algorithm. Thus, this paper recommends the NCRP(55/50) algorithm as an optimal two-dosimeter algorithm in consideration of the solid technical background of NCRP and the convenience of radiation works. In addition, it was determined that a two-dosimeter is provided to a single task which is expected to produce a dose rate of more than 1 mSv/hr, a difference of dose rates depending on specific parts of the body of more than 30%, and an exposure dose of more than 2 mSv.】
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".