Improving systematic predictions of<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>β</mml:mi></mml:math>-delayed neutron emission probabilities
Why this work is in the frame
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Bibliographic record
Abstract
The probability ${P}_{n}$ of emitting a neutron following $\ensuremath{\beta}$ decay is critical in many areas of nuclear science, from understanding nucleosynethesis during the $r$ process to control of reactor power levels and nuclear waste management. As it is not always easy to measure or calculate, indirect empirical approaches have been developed to estimate the ${P}_{n}$ value from the decay ${Q}_{\ensuremath{\beta}}$ value and the neutron separation energy S${}_{n}$. Here, we present a new prescription incorporating also the half-life ${T}_{1/2}$, which correlates the known data better and thus improves an estimation of ${P}_{n}$ when only ${T}_{1/2}$, ${Q}_{\ensuremath{\beta}}$, and S${}_{n}$ are known. This new relation can be used to predict ${P}_{n}$ values for cases where the half-life is known, thus it can be useful in $r$-process network calculations and in modeling advanced fuel cycles.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it