Robotic Dispersal Technique for 35 GBq of 140La in an L-polygon Pattern
Bibliographic record
Abstract
In 2018, Defence Research and Development Canada, in partnership with Natural Resources Canada, led a field trial of survey and mapping of a large dispersion of radioactivity using Unmanned Aerial Vehicles (UAVs). The intent was to disperse La material in a 3,200 m L-polygon with an approximate activity level of 10 MBq m and to measure the radioactive material using sensors carried by UAVs. Due to the potential radiological hazard to personnel, the activity was approved only if Unmanned Ground Vehicles (UGVs) were able to completely handle and disperse the material remotely. One UGV was equipped with a traditional agricultural sprayer to disperse the material, and one UGV was equipped with a force feedback manipulator arm. Due to the freezing temperatures during dispersal, the 35 GBq of La was dispersed non-uniformly as one sprayer boom failed to perform as tested. However, rough analysis of the electronic dosimetry on the UGV concluded that 99% of the material was dispersed on the ground. The dosimeter placed closest to the robot manipulator arm, used for dispersal of material, indicated a contact dose of 33.5 mSv. The electronic dosimeter placed where the driver would have sat on the sprayer vehicle if it were not unmanned indicated a dose of 22.3 mSv. Thus, the use of UGVs for material dispersion substantially reduced the external exposure to personnel. The use of UGVs eliminated the potential of internal exposure as well. The Radiation Safety Officer received the highest dose at approximately 3 μSv, with the majority of the exposure coming from the handling of the Type A container.
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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 | 0.000 | 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) | 0.001 | 0.001 |
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".