Modelling the atmospheric dispersion of radiotracers in small-scale, controlled detonations: validation of dispersion models using field test data
Bibliographic record
Abstract
Abstract A series of modelling exercises, based on field tests conducted in the Czech Republic, were carried out by the ‘Urban’ Working Groups as part of the International Atomic Energy Agency’s Environmental Modelling for Radiation Safety II, Modelling and Data for Radiological Impact Assessment (MODARIA) I and MODARIA II international data compilation and model validation programmes. In the first two of these programmes, data from a series of field tests involving dispersion of a radiotracer, 99mTc, from small-scale, controlled detonations were used in a comparison of model predictions with field measurements of deposition. In the third programme, data from a similar field test, involving dispersion of 140La instead of 99mTc, were used. Use of longer-lived 140La as a radiotracer allowed a greater number of measurements to be made over a greater distance from the dispersion point and in more directions than was possible for the earlier tests involving shorter-lived 99mTc. The modelling exercises included both intercomparison of model predictions from several participants and comparison of model predictions with the measured data. Several models (HotSpot, LASAIR, ADDAM/CSA-ERM, plus some research models) were used in the comparisons, which demonstrated the challenges of modelling dispersion of radionuclides from detonations and the need for appropriate meteorological measurements.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".