Neutron-activation revisited: The depletion and depletion-activation models
Bibliographic record
Abstract
The growth of a radioactive daughter in neutron activation is commonly described with the saturation model that ignores the consumption of parent nuclei during the radio-activation process. This approach is not valid when radioactive sources with high specific activities are produced or when the particle fluence rates used are very high. Assuming a constant neutron fluence rate throughout the activation target, a neutron-activation model that accounts for the depletion in parent nuclei is introduced. This depletion model is governed by relationships similar to those describing the parent-daughter-granddaughter decay series, and, in contrast to the saturation model, correctly predicts the practical limit of the daughter specific activity, irrespective of the particle fluence rate. Also introduced is a neutron-activation model that in addition to parent depletion accounts for the neutron activation of daughter nuclei in situations where the cross section for this effect is high. The model is referred to as the depletion-activation model and it provides the most realistic description for the daughter specific activity in neutron activation. Three specific neutron activation examples of interest to medical physics are presented: activation of molybdenum-98 into molybdenum-99 described by the saturation model; activation of cobalt-59 into cobalt-60 described by the depletion model; and activation of iridium-191 into iridium-192 described by the depletion-activation model.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".