Modeling Population Screening Process for Maximizing Throughputs
Bibliographic record
Abstract
Following a large-scale radiation emergency, affected populations will need to be screened soon after for potential contamination (external or internal). Effective management of the available resources can help maximize the screening throughputs. This paper reports the modeling results for screening throughputs in a population screening center using a set resource, considering two major variables, the arrival rate (number of people arriving at the screening center per minute) and the contamination probability (the probability of finding a contaminated group). Both the full process (including all sub-processes in a population screening center) and the core process (including only the screening sub-processes: pre-screening, portal monitoring, and whole body counting) were simulated. As expected, for both processes, as the arrival rate increases, the screening center can get overwhelmed. Interestingly, the contamination probability becomes a significant factor for screening throughputs only when the arrival rate becomes high. The results show that following an emergency, when the arrival rate is high, much more resources will need to be deployed to the population screening center or multiple screening centers will need to be established.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".