Bibliographic record
Abstract
In order to provide effective monitoring and follow-up on the health effects of individuals accidentally exposed to ionising radiation, a Radiation Accident Registry (RAR) has been designed and constructed as an extension to the existing National Dose Registry (NDR). The RAR has basic functions of recording, monitoring and reporting. This type of registry is able to assist responders in preparing for and managing situations during radiological events and in providing effective follow-up on the long-term health effects of persons exposed to ionising radiation. It is especially important to register radiation-exposed people in vulnerable population groups, such as children and pregnant women, to ensure proper long-term health care and protection. Even though radiation accidents are rare, a registry prepared for such accidents could involve a large population and, in some cases, require lifetime monitoring for individuals. One of the most challenging tasks associated with RAR is the assessment of radiation dose resulting from accidents. In some cases, the assessment of radiation doses to individuals could be a process requiring the involvement of various methods. The development of fast and accurate dose assessment tools will remain a long-term challenge associated with the RAR. To meet this challenge, further research activities in radiation dosimetry for individual monitoring are needed.
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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 | 0.043 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".