Computer Aided Dosimetry and Verification of Exposure to Radiation
Bibliographic record
Abstract
In the timeframe following the September 11th attacks on the United States, increased emphasis has been placed on Chemical, Biological, Radiological and Nuclear (CBRN) preparedness. Of prime importance is rapid field assessment of potential radiation exposure to Canadian Forces field personnel. This work set up a framework for generating an "expert" computer system for aiding and assisting field personnel in determining the extent of radiation insult to military personnel. Data was gathered by review of the available literature, discussions with medical and health physics personnel having hands-on experience dealing with radiation accident victims, and from experience of the principal investigator. Flow charts and generic data fusion algorithms were developed. Relationships between known exposure parameters, patient interview and history, clinical symptoms, clinical work-ups, physical dosimetry, biological dosimetry, and dose reconstruction as critical data indicators were investigated. The data obtained was examined in terms of information theory. A main goal was to determine how best to generate an adaptive model (i.e. when more data becomes available, how is the prediction improved). Consideration was given to determination of predictive algorithms for health outcome. In addition. the concept of coding an expert medical treatment advisor system was developed (U)
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".