AMS/NRCan Joint Survey Report: Aerial Campaign
Bibliographic record
Abstract
In January 2014 the U.S. Department of Energy (DOE), National Nuclear Security Administration (NNSA) Aerial Measuring System (AMS) and the Natural Resources Canada (NRCan) Nuclear Emergency Response project conducted a series of joint surveys at a number of locations in Nevada including the Nevada National Security Site (NNSS). The goal of this project was to compare the responses of the two agencies’ aerial radiation detection systems and data analysis techniques. This test included varied radioactive surface contamination levels and isotopic composition experienced at the NNSS and the differing data processing techniques utilized by the respective teams. Because both teams used the commercial aerial radiation detection systems from Radiation Solutions, Inc., the main focus of the campaign was to investigate the data acquisition techniques, data analysis, and ground-truth verification. The NRCan system consisted of four 4" × 4" × 16" NaI(Tl) scintillator crystals of which two were externally mounted in a modified commercial cargo basket certified for the Eurocopter AS350; the NNSA AMS system consisted of twelve 2" × 4" × 16" NaI(Tl) crystals in externally mounted dedicated pods. For NRCan, the joint survey provided an opportunity to characterize their system’s response to extended sources of various fission products at the NNSS. Since both systems play an important role in their respective countries’ national framework of radiological emergency response and are subject to multiple mutual cooperation agreements, it was important for each country to obtain more thorough knowledge of how they would employ these important assets and define the roles that they would each play in an actual response.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".