EFFECT OF MASS, AT A FIXED HEIGHT, ON THE COUNTING EFFICIENCY OF A BOMAB PHANTOM IN THREE TYPES OF WHOLE BODY COUNTER MODELED BY MCNP5
Bibliographic record
Abstract
Using demographic data, a series of BOMAB phantoms were developed to study the effect of size, at a fixed height, of a BOMAB phantom using Monte Carlo simulations in three different whole body counting systems: the HML's scanning detector whole body counter, a FastScan whole body counter, and a StandFast whole body counter. The latter has had two counting geometries simulated--one for the recommended position, and another simulating a constant detector-to-front-of-phantom distance. The six phantom sizes corresponded to the following masses: 48 kg, 73 kg, 98 kg, 123 kg, 148 kg, and 173 kg. The effect of size varies with photon energy, as might be expected, and at any given energy is an exponential function of the mass. An equation has been found that fits most cases very well and is still good in poorer cases. Persons lighter in mass than the normal calibration phantom (73 kg) will have their body burdens overestimated by as much as a factor of 1.3, depending on mass, photon energy, and counting geometry. Conversely, heavier individuals will have their body burdens underestimated by as much as a factor of 1.9, depending on mass, photon energy, and counting geometry.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".