CHALLENGES FOR PHYSICS-BASED MODELS OF A RADIONUCLIDE DISPERSAL DEVICE
Bibliographic record
Abstract
A “dirty bomb” is a type of hypothetical radiological dispersal device (RDD) that has been the subject of significant safety and security concerns given the disruption that would result in a postulated terrorist attack. Reliable and accurate predictions of dispersion of radiological material from an RDD are absolutely necessary for first responders and emergency decision makers to plan effective response strategies. Development of high-fidelity, mechanistic models of a dirty bomb are complicated because dispersion over areas with the greatest risk of contamination is highly sensitive to the source of contaminant particles, and this source term is governed by processes over much smaller temporal and spatial length scales than the dispersion. New work on accelerating high-fidelity models of RDDs has been initiated that looks to incorporate the multiscale aspects of the problem and enhance predictive capabilities that may assist in anti-terrorism activities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".