LESSONS LEARNED FROM THE EURADOS SURVEY ON INDIVIDUAL MONITORING DATA AND INTERNAL DOSE ASSESSMENTS OF FOREIGNERS EXPOSED IN JAPAN FOLLOWING THE FUKUSHIMA DAIICHI NPP ACCIDENT
Bibliographic record
Abstract
European Radiation Dosimetry Group e.V. (EURADOS) survey on individual monitoring data and dose assessment has been carried out for 550 foreigners returning home after being exposed in Japan to intakes of radionuclides (mainly (131)I, (132)I, (132)Te, (134)Cs and (137)Cs) as a consequence of the Fukushima Daiichi NPP accident. In vivo and in vitro measurements were performed in their respective countries at an early stage after that accident. Intakes of radionuclides were detected in 208 persons from Europe and Canada, but the committed effective dose E(50) was below the annual dose limit for the public (<1 mSv) in all the cases. Lessons learned from this EURADOS survey are presented here regarding not only internal dosimetry issues, but also the management of the emergency situation, the perception of the risk of health effects due to radiation and the communication with exposed persons who showed anxiety and lack of trust in monitoring data and dose assessments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".