GHSI EMERGENCY RADIONUCLIDE BIOASSAY LABORATORY NETWORK: SUMMARY OF A RECENT EXERCISE
Bibliographic record
Abstract
The Global Health Security Initiative (GHSI) established a laboratory network within the GHSI community to develop their collective surge capacity for radionuclide bioassay in response to a radiological or nuclear emergency. A recent exercise was conducted to test the participating laboratories for their capabilities in screening and in vitro assay of biological samples, performing internal dose assessment and providing advice on medical intervention, if necessary, using a urine sample spiked with a single radionuclide, 241Am. The laboratories were required to submit their reports according to the exercise schedule and using pre-formatted templates. Generally, the participating laboratories were found to be capable with respect to rapidly screening samples for radionuclide contamination, measuring the radionuclide in the samples, assessing the intake and radiation dose, and providing advice on medical intervention. However, gaps in bioassay measurement and dose assessment have been identified. The network may take steps to ensure that procedures and practices within this network be harmonised and a follow-up exercise be organised on a larger scale, with potential participation of laboratories from the networks coordinated by the International Atomic Energy Agency and the World Health Organization.
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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 | 0.040 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".