A methodology for improving throughput using portal monitors
Bibliographic record
Abstract
The National Internal Radiation Assessment Section (NIRAS), which operates the Canadian National Calibration Reference Centre for Bioassay and In Vivo Monitoring, has field deployable equipment for emergency response. A substantial part of this tool kit is a set of portal monitors that can be used to quickly screen people into the 'uncontaminated' and the 'contaminated'. The former term refers to a person who has <60 kBq (empirical practical detection limit) of activation/fission products and the latter group is contaminated by that amount or more. Recent field work has shown that one type of the NIRAS's portal monitors can be alarmed at significant distances if the level of contamination is high enough. The other types, which do not initiate a count until either an infra-red beam is broken or a proximity detector is activated, do not alarm but their background will be raised and this causes other problems. This paper proposes a method of group monitoring to help speed up the process of screening a large number of potentially contaminated persons using portal monitors. In short, the group of potentially contaminated persons will be kept isolated from the portal stations. Depending on a real-time estimate of the percentage of contaminated persons in the crowd, groups of persons will be selected for screening. The hypergeometric distribution has been used to decide on the sampling group size with an expectation that 90% of the time no contaminated person will be present in the group. Once removed from the main waiting area, the group will be pre-screened and then, depending on the result, sent to the appropriate portal. It is anticipated that this will greatly speed up processing as it substantially reduces the transit time. Transits times have also been estimated in addition to the number of personnel required to run all of NIRAS's field deployable equipment.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".