Aerial Mobile Radiation Survey Following Detonation of a Radiological Dispersal Device
Bibliographic record
Abstract
A series of experiments was conducted in 2012 at the Defence Research and Development Canada's Suffield Research Centre in Alberta, Canada, during which three radiological dispersal devices were detonated. The detonations released radioactive (140)La into the air, which was then carried by winds and detectable over distances of up to 2 km. The Nuclear Emergency Response group of Natural Resources Canada conducted airborne radiometric surveys shortly following the explosions to map the pattern of radioactivity deposited on the ground. The survey instrument suite was based on large volume NaI(Tl) scintillation gamma radiation detectors, which were situated in a basket mounted exterior to the helicopter and oriented end-to-end to maximize the sensitivity. A standard geophysical data treatment was used to subtract backgrounds and to correct the data to produce counts due to (140)La at the nominal altitude. Sensitivity conversion factors obtained from Monte Carlo simulations were then applied to express the measurements in terms of surface activity concentration in kBq m(-2). Integrated over the survey area, the results indicate that only 20 to 25% of the bomb's original inventory of radioactive material is deposited within a 1.5-km radius of ground zero. These results can be accommodated with a simple model for the RDD behavior and atmospheric dispersion.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".