Radiological/Nuclear Human Monitoring Tabletop Exercise
Bibliographic record
Abstract
Health Canada is the lead department for coordinating the federal response to a Canadian nuclear emergency event. The framework to manage a radiological consequence is outlined in the Federal Nuclear Emergency Plan (FNEP). In 2014, a full scale exercise (FSX) was held to test the capacity of the federal government to handle a nuclear facility emergency disaster in Canada. The FSX provided a means to demonstrate the integration of various departments and agencies in response to such an event, and although a number of task teams within FNEP were tested, the capacity to monitor humans for exposure post-event was not played out fully. To address this, a table top exercise (TTX) was held in 2015 that brought together experts from human monitoring groups (HMGs) in partnership with Provincial and Municipal emergency response organizations. The TTX took the form of a facilitated discussion centered around two types of radiological/nuclear (RN) emergency scenarios that commenced post-release. The purpose of the exercise was to integrate these communities and identify knowledge gaps in policies and concepts of operations pertaining to the human monitoring aspects of RN events including biodosimetry, bioassay, portal monitors, whole body counting, and the provision of personal dosimetry. It also tested the interoperability between first responders/receivers and Federal, Provincial, and Municipal emergency response organizations. The end outcome was the identification of clear knowledge gaps in existing and newly developed concepts of operation in the human population monitoring response to an RN emergency in Canada; these and possible recommendations are captured in this report.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.066 | 0.009 |
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".